METHOD AND SYSTEM FOR FORECASTING POWER REQUIREMENTS USING GRANULAR METRICS

PROCEDE ET SYSTEME DE PREVISION DES BESOINS EN ENERGIE A L'AIDE DE MESURES GRANULAIRES

English Abstract

A method for modeling power usage within a macrogrid uses data relating to the behavioral patterns and states ("BA") of the users, data relating to external impacts on power usage and disaggregated power consumption data in at least one premises within the macrogrid (forming "power usage model data") and thereafter a method of forecasting and predicting future power requirements within the macrogrid uses such power usage model data.

French Abstract

L'invention concerne un procédé pour modéliser la consommation électrique dans un macroréseau consistant à utiliser des données relatives aux types et états de comportements (« BA ») des utilisateurs, des données relatives aux influences externes sur la consommation électrique et des données désagrégées sur la consommation électrique dans au moins un local à l'intérieur du macroréseau (formant des « données de modèle de consommation électrique »). L'invention concerne également un procédé de prévision et de prédiction des futurs besoins en énergie dans le macroréseau qui consiste à utiliser de telles données de modèle de consommation électrique.

Claims

CLAIMS
1. A method of regulating power supply and distribution across a
macrogrid, the macrogrid being within a region, the method comprising:
a) obtaining, by a sensor, power consumption data from a premises
within the macrogrid;
b) disaggregating, by a processor, the power consumption data to
determine at least one behavioral pattern according to usage behaviors for at
least two appliances within the premises by:
storing, in a database, power signal clips of models of said at
least two appliances;
creating, for each of the appliances, a model power draw using
a Hidden Markov Model (HMM);
estimating a Factorial HMM comprising an HMM chain for each
of said at least two appliances, each HMM chain representing a power
draw of the respective appliance, wherein the estimating comprises
estimating each HMM chain using (i) an Explicit Duration HMM with
Difference Observations and (ii) the model power draw of the
respective appliance;
tuning one or more parameters of said Factorial HMM based on
a comparison of signal clips of power consumed at the premises with
the power signal clips of the models of said at least two appliances;
calculating a difference between a total power draw of the
appliances at a first time and the total power draw of the appliances at
a preceding time; and
determining whether a state of exactly one of the appliances has
changed, based on said difference;
c) obtaining, by the processor, data relating to the at least one
behavioral pattern and a state of a user;
d) obtaining, by the processor, data relating to an impact on the user's
power usage within the premises, said impact originating from an environment
of the user and not from the user;
e) using, by the processor, the obtained data from one or more of b) to
d) to create an individual consumer forecast of power usage, said consumer
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forecast being collectively aggregated across a plurality of users;
f) using, by the processor, data from one or more of b) to e) to perform
a demographic analysis;
g) collecting, by the processor, macrogrid aggregate power
consumption data for the region;
h) determining, by the processor, power consumption requirements
across the macrogrid for the region using data from at least one of b) to g);
i) determining, by the processor, future power requirements within the
macrogrid, using the data obtained in at least one of b) to h); and
j) regulating, by the processor, power supply and distribution across the
macrogrid using said future power requirements.
2. The method of claim 1 wherein said determining the future power
requirements is performed for at least one the following: decreasing and
tracking consumer usage of a power resource, decreasing consumer costs for
power, maximizing revenue for a power provider, minimizing consumer
discomfort, and identifying future peak usage times and requirements.
3. The method of claim 1 wherein said determining whether the state of
exactly one of the appliances has changed is also based on a duration of how
long said one appliance has been in the state up to said first time.
4. A method of regulating power supply and distribution across a
macrogrid for a region, the method comprising:
a) periodically obtaining, by sensors, granular power consumption data
from premises of a statistically significant portion of a target population of

power users within the macrogrid for the region;
b) disaggregating, by a processor, the power consumption data to
determine usage behaviors for at least two appliances at one of the premises
by:
storing, in a database, power signal clips of models of said at
least two appliances;
creating, for each of the appliances, a model power draw using
a Hidden Markov Model (HMM);
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estimating a Factorial HMM comprising an HMM chain for each
of said at least two the appliances, each HMM chain representing a
power draw of the respective appliance, wherein the estimating
comprises estimating each HMM chain using (i) an Explicit Duration
HMM with Difference Observations and (ii) the model power draw of
the respective appliance;
tuning one or more parameters of said Factorial HMM based on
a comparison of signal clips of power consumed at said one of the
premises with the power signal clips of the models of said at least two
appliances;
calculating a difference between a total power draw of the
appliances at a first time and the total power draw of the appliances at
a preceding time; and
determining whether a state of exactly one of the appliances has
changed, based on said difference;
c) obtaining, by the processor, data relating to at least one behavioral
pattern and a state of a user;
d) obtaining, by the processor, data relating to an impact on the user's
power usage within said one of the premises, said impact originating from an
environment of the user and not from the user;
e) using, by the processor, obtained data from at least one of a) to d) to
generate an individual consumer forecast of power usage, the consumer
forecast being collectively aggregated across a plurality of users;
f) using, by the processor, data from one or more of a) to e) to perform
a demographic analysis;
g) collecting, by the processor, aggregate power consumption data for
the region;
h) determining, by the processor, power consumption requirements
across the macrogrid for the region using data from at least one of more of a)

to g);
i) determining, by the processor, future power requirements within the
macrogrid, using the data obtained in at least one of a) to h); and
j) regulating, by the processor, power supply and distribution across the
macrogrid using said future power requirements.
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5. The method of claim 4 wherein periodically comprises receiving data
within a five minute interval or less.
6. The method of claim 5 wherein said impact is selected from any one of:
weather patterns and predictions, calendar month, economic indicators, world
events, television programming, equipment age and maintenance, and
population mobility.
7. The method of claim 5 wherein the at least one behavioral pattern
comprises data related to user behavioral states selected from one or more of
the following states: sleeping, just awoken, on vacation, working from home,
charging electric vehicles, and extended absence.
8. The method of claim 4 wherein periodically comprises receiving data
within a one hour interval or less.
9. The method of claim 4 wherein said determining the future power
requirements is performed for at least one the following: decreasing and
tracking consumer usage of a power resource, decreasing consumer costs for
power, maximizing revenue for a power provider, minimizing consumer
discomfort, and identifying future peak usage times and requirements.
10. The method of claim 4 wherein the target population is selected from at

least one of: consumers, commercial operations and industrial operations.
11. The method of claim 4, wherein the statistically significant portion is
a
population sample which is larger than or equal to a smallest possible sample
for which the behavioral patterns of an entire population of power users
within
the macrogrid can be observed and modeled within a required accuracy.
12. A system for modeling power usage within a macrogrid for a region, for
determining future power requirements within the macrogrid and regulating
power supply and distribution across the macrogrid, the system comprising:
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a sensor that obtains power consumption data from a premises within
the macrogrid;
a server and one or more databases, the server comprising a
processor and memory, the memory comprising computer executable
instructions which, when executed by the processor cause the server to:
a) disaggregate the power consumption data to determine at least one
behavioral pattern according to usage behaviors for at least two appliances
within said premises by:
storing, in a database, power signal clips of models of said at
least two appliances;
creating, for each of the appliances, a model power draw using
a Hidden Markov Model (HMM);
estimating a Factorial HMM comprising an HMM chain for each
of said at least two appliances, each HMM chain representing a power
draw of the respective appliance, wherein the estimating comprises
estimating each HMM chain using (i) an Explicit Duration HMM with
Difference Observations and (ii) the model power draw of the
respective appliance;
tuning one or more parameters of said Factorial HMM based on
a comparison of signal clips of power consumed at the premises with
the power signal clips of the models of said at least two appliances;
calculating a difference between a total power draw of the
appliances at a first time and the total power draw of the appliances at
a preceding time; and
determining whether a state of exactly one of the appliances has
changed, based on said difference;
b) obtain data relating to the at least one behavioral pattern and a state
of a user;
c) obtain data relating to an impact on the user's power usage within
the premises, said impact originating from an environment of the user and not
from the user;
d) use the obtained data from one or more of a) to c) to create an
individual consumer forecast of power usage, said consumer forecast being
collectively aggregated across a plurality of users;

e) use data from one or more of a) to d) to perform a demographic
analysis;
f) collect macrogrid aggregate power consumption data for the region;
g) determine power consumption requirements across the macrogrid
for the region using data from at least one of a) to f);
h) determine future power requirements within the macrogrid, using the
data obtained in at least one of a) to g); and
i) regulate power supply and distribution across the macrogrid using
said future power requirements.
13. The system of claim 12, wherein said impact is selected from any one
of: weather patterns and predictions, calendar month, economic indicators,
world events, television programming, equipment age and maintenance, and
population mobility.
14. The system of claim 12 wherein the at least one behavioral pattern
comprises data related to user behavioral states selected from one or more of
the following states: sleeping, just awoken, on vacation, working from home,
charging electric vehicles, and extended absence.
15. The system of claim 12 wherein the at least one behavioral pattern
comprises data related to user behavioral patterns selected from one or more
of the following patterns: sleeping hours, weekend schedules, weekday
schedules, departure to work and arrival home from work, and amount of time
spent away from home.
16. The system of claim 12 wherein a forecast period is selected as being
one of: minutes, hours, days, months, seasonal and years.
17. The system of claim 12 further comprising instructions that cause the
server to use the forecast to identify timing and location of future upgrades.
18. The system of claim 12 further comprising instructions that cause the
server to use the forecast to dynamically evaluate power consumption
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patterns in a selected demographic sector of the population.
19. The system of claim 12 further comprising instructions that cause the
server to interpolate a sample user forecast onto larger populations with at
least one diverse demographic variation.
20. The system of claim 12 further comprising instructions that cause the
server to use the forecast to identify one or more periods in a user's billing

cycle in which a potential for energy conservation exists for that user.
21. The system of claim 12 further comprising instructions that cause the
server to use the forecast to identify a power consumption reduction and
budget reduction plan.
22. A system for forecasting and predicting power usage within a
macrogrid, and regulating power supply and distribution across the macrogrid,
the system comprising:
sensors that obtain granular power consumption data from premises of
a statistically significant portion of a target population of power users
within
the macrogrid;
a server and one or more databases, the server comprising a
processor and memory, the memory comprising computer executable
instructions which, when executed by the processor cause the server to:
a) periodically receive the granular power consumption data;
b) use the power consumption data to perform a consumer load
disaggregation to determine usage behaviors for at least two appliances in
one of said premises by:
storing, in a database, power signal clips of models of said at
least two appliances;
creating, for each of the appliances, a model power draw using
a Hidden Markov Model (HMM);
estimating a Factorial HMM comprising an HMM chain for each
of said at least two appliances, each HMM chain representing a power
77

draw of the respective appliance, wherein the estimating comprises
estimating each HMM chain using (i) an Explicit Duration HMM with
Difference Observations and (ii) the model power draw of the
respective appliance;
tuning one or more parameters of said Factorial HMM based on
a comparison of signal clips of power consumed said one of the
premises with the power signal clips of the models of said at least two
appliances;
calculating a difference between a total power draw of the
appliances at a first time and the total power draw of the appliances at
a preceding time; and
determining whether a state of exactly one of the appliances has
changed, based on said difference;
c) obtain data relating to at least one behavioral pattern and a state of
a user;
d) obtain data relating to an impact on the user's power usage within
said one premises, said impact originating from an environment of the user
and not from the user;
e) use obtained data from at least one of a) to d) to generate an
individual consumer forecast of power usage, the consumer forecast being
collectively aggregated across a plurality of users;
f) use data from one or more of a) to e) to perform a demographic
analysis;
g) collect aggregate power consumption data for a region associated
with the macrogrid;
h) determine power consumption requirements across the macrogrid
for the region using data from at least one of more of a) to g);
i) determine future power requirements within the macrogrid, using the
data obtained in at least one of a) to h); and
j) regulate power supply and distribution across the macrogrid using
said future power requirements.
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Description

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METHOD AND SYSTEM FOR FORECASTING POWER REQUIREMENTS USING
GRANULAR METRICS
Field of the Invention
[0001]The present invention to the field of power monitoring and data
analytics in
the area of power management.
Background of the Invention
(0002] Utility companies are obligated to create demand projections and
maintain
a regulated reserve margin above it. The capacity that is above or below that
margin can be bought or sold in the energy markets.
[0003]Furthermore, there is a growing tendency towards unbundling the power
system as different sectors of the industry (generation, transmission, and
distribution) are faced with increasing demand on planning management and
operations of the networks. The operation and planning of a power utility
company requires an adequate model for power load forecasting. This load
forecasting plays a key role in helping a utility to make important decisions
on
power, load switching, voltage control, network reconfiguration, and
infrastructure
development.
(0004] Methodologies of load forecasts can be divided into various categories
which include short-term forecasts, medium-term forecasts, and long-term
forecasts. For example, short-term forecasting gives a forecast of load about
one
hour ahead of time. Such a forecast may assist in making decisions aimed at
preventing an imbalance in the power generation and load demand, which would
lead to greater network reliability and power quality.
(0005]Many methods have been used for load forecasting. These include
statistical methods such as regression and similar-day approach, fuzzy logic,
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expert systems, support vector machines, econometric models, and end-use
models.
[0006]New power forecasting models have been introduced such as artificial
intelligence (Al), artificial neural network (ANN), and support vector
machines
(SVM).
[0007]An ANN is trained on input data as well as the associated target values.

The trained network can then make predictions based on the relationships
learned during training. Generally, ANN refers to a class of models inspired
by the
biological nervous system. The models are composed of many computing
elements, usually denoted neurons; each neuron has a number of inputs and one
output. It also has a set of nodes called synapses that connect to the inputs,

output, or other neurons.
[0008]A linear combiner is used to produce a single value from all the inputs.
The
single value is the weighted sum of the inputs from which the threshold value
associated with the neurons is subtracted to compose the activation of the
neuron. The activation signal is passed through an activation function to
produce
the output of the neuron. The chosen activation function is normally a non-
linear
function (for example, a sigmoid function), a feature that allows the ANN to
represent more complex problems.
[0009]Most ANN models focus in connection with short-term forecasting use
multi-layer perceptron (MLP) networks. The attraction of MLP can be explained
by
the ability of the network to learn complex relationships between input and
output
patterns, which would be difficult to model with conventional methods. Inputs
to
the networks are generally present and past load values. The network is
trained
using actual load data from the past.
[0010]Within the power demand forecasting context, ANN uses data such as total

regional demand for energy, weather, daylight hours, and large community
events
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to project the short terms electricity demand of a given region. The mean
error for
this type of forecast is popularly reported as 1.5%, which may cost a utility
millions of dollars in losses annually
[0011]There are significant other drawbacks to the ANN and like systems. One
of
the most salient of these is the ongoing requirement to have nothing impacting
on
the network which would lead to a loss of its generalizing capability.
[0012] There are a number of short-term load forecasting algorithms for
utility
applications and energy trading, such as ANNSTLF by Electric Power Research
Institute (EPRI), and NOSTRADAMUS by Ventyx. These use ANN to model the
regional load demand, primarily using load history and weather as inputs.
[00131 Moreover, there has been research in the area of power prediction and
energy optimization using microgrids, or small, localized groups of energy
generators/storers. Microgrid is defined as a system as follows:
"--designed, built, and controlled by "customers" based on internal
requirements
--subject to the technical, economic, and regulatory opportunities and
constraints
faced
--a cluster of small (e.g. <500 kW) sources, storage systems, and loads which
presents itself to the grid as a legitimate entity, i.e. as a good citizen
interconnected with the familiar wider power system, or macro grid, but can
island
from it.
The Micro Grid concept assumes a cluster of loads, micro-sources and storage
operating as a single system.. "1
I Presented to the grid as a single controllable unit (impacts system
reliability Microgrids and the Macrogrid Presentation
to theCalifornia Public Utilities Commission 20 February 2001 By Abbas Akhil,
Chris Marnay, & Bob LasseterSandia
National Laboratory, Berkeley Lab, and University of Wisconsin, Madison
Consortium for Electric Reliability Technology
Solutions (available publicly online at vam.pserr.wisc.ed u/d ocume ntsjg
enera 1...by.õ/certssintupt)
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[00141At this microgrid type level, monitoring and assessment of individual
users
and their loads is undertaken and is feasible. This type of system cannot be
reliably or practically used to forecast within a macrogrid.
[0015] From the perspective of the consumer, as opposed to utility companies,
there are some overlapping but also different concerns in regards to power
usage. With the advent of "smart grid" technologies, also called "smart home",

"smart meter", or "home area network" (HAN) technologies, optimized demand
reductions became possible at the end use or appliance level. Smart grid
technologies provided the ability to capture real-time or near-real-time end-
use
data and enabled two-way communication. Smart grid technologies currently
exist
for at least some percentage of a utility's customer base and applications are

growing throughout North America.From a consumer perspective, smart metering
offers a number of potential benefits to householders. These include a) An end
to
estimated bills, which are a major source of complaints for many customers b)
A
tool to help consumers better manage their energy use - smart meters with a
display can provide up to date information on gas and electricity consumption
in
the currency of that country and in doing so help people to better manage
their
energy use and reduce their energy bills and carbon emissions
[0016] Electricity pricing usually peaks at certain predictable times of the
day and
the season. In particular, if generation is constrained, prices can rise from
other
jurisdictions or more costly generation is brought online. It is believed that
billing
customers by time of day will encourage consumers to adjust their consumption
habits to be more responsive to market prices. Regulatory and market design
agencies hope these "price signals" will delay the construction of additional
generation or at least the purchase of energy from higher priced sources,
thereby
controlling the steady and rapid increase of electricity prices
[0017] Using smart grid technologies, a system operator can optimally and
dynamically dispatch on and off signaling to specific appliances at a customer
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location given the observed and forecast loads of other appliances on a
circuit or
system.
[0018]It is an object of the present invention to obviate or mitigate the
above
disadvantages and to provide solutions for modeling and forecasting in the
provision of power resources.
Summary of the Invention
[0019] In one aspect, the present invention provides a method for modeling
power usage within a macrogrid, using data relating to the behavioral patterns

and states ("BA") of the users, data relating to external impacts on power
usage
and disaggregated power consumption data in at least one premises within the
macrogrid (forming "power usage model data') and thereafter forecasting and
predicting future power requirements within the macrogrid, using such power
usage model data.
[0020]The present invention provides, in one embodiment, a computer
implemented method of modeling power usage within a macrogrid, wherein
macrogrid is within a region, which comprises:
a) acquiring and storing disaggregated power consumption data in at least one
premise, within the macrogrid (load disaggregation, "LD") thereby to determine

usage behaviors for appliances and devices by users within the macrogrid
("behavioral patterns");
b) collecting and compiling data relating to the behavioral patterns and
states
("BA") of the users;
c) collecting and compiling data relating to external impacts on power usage;
d) using data from one or more of a) to c), in any combination, to create by a

processor, an individual consumer forecast ("CF") of power usage, said CF
being
collectively aggregated across the users;
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e) using data from one or more of a) to e), in any combination, to perform and

aggregate by a processor a demographic analysis ("DA")'
f) collecting macrogrid aggregate power consumption data for the region;
g) calculating and predicting power consumption requirements across the
macrogrid for said region using data from at least one of steps a) to e); and
h) forecasting and predicting future power requirements within the macrogrid,
using such data as acquired in at least one of steps a) to g).
[0021]The present invention provides, in another aspect, a computer
implemented method of modeling power usage within a macrogrid which
comprises:
a) receiving and compiling by a processor in real-time granular power
consumption data from a statistically significant portion of a target
population of
power users (the "users") within the macrogrid (the "granular data") for a
region;
b) using the granular data and computer processor to perform consumer load
disaggregation ("LD") thereby to determine usage behaviors for appliances and
devices by said users ("behavioral patterns");
c) collecting and compiling data relating to the behavioral patterns and
states
("BA") of the users;
d) collecting and compiling data relating to external impacts on power usage;
e) using data from at least one of steps a) to d), in any combination, to
create by a
processor, an individual consumer forecast ("CF")of power usage, said CF being

collectively aggregated across the users;
f) using data from one or more of a) to e), in any combination, to perform and

aggregate by a processor a demographic analysis ("DA")'
g) collecting (macrogrid) aggregate power consumption data for said region;
h) calculating and predicting power consumption requirements across the
macrogrid for said region using data from at least one of more of a) to g);
and
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i) forecasting and predicting future power requirements within the macrogrid,
using such data as acquired in at least one of steps a) to h).
[0022]The present invention provides, in another aspect, a system for modeling

power usage within a macrogrid for the purpose of forecasting and predicting
future power requirements within the macrogrid which comprises a server and
one or more databases; wherein said server executes, in real time, a method
comprising:
a) receiving and compiling disaggregated power consumption data in a
premises, within the macrogrid (load disaggregation, "LD") thereby to
determine usage behaviors for appliances and devices by users within a
macrogrid ("behavioral patterns)
b) collecting and compiling data relating to the behavioral patterns and
states
("BA") of the users;
c) collecting and compiling data relating to external impacts on power usage
by
the users;
d) using data from at least one a) to c), in any combination, to create an
individual consumer forecast ("CF") of power usage for one user within all of
users within the macrogrid, said CF being collectively aggregated across the
users;
e) using data from at least one of a) to d), in any combination, to perform
and
aggregate a demographic analysis ("DA")'
0 collecting (macrogrid) regional aggregate power consumption data;
g) calculating and predicting power consumption requirements across the
macrogrid using data from one or more of a) to f); and
h) forecasting and predicting future power requirements within the macrogrid,
using such data as acquired in at least one of steps a) to g).
[0023]The present invention provides, in another aspect, a system for
forecasting
and predicting power usage within a macrogrid which comprises a server and one
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or more databases; wherein said server executes, in real time, a method
comprising:
a) receiving and compiling real time granular power consumption data from a
statistically significant portion of a population of power users (the "users")
within
the macrogrid (the "granular data");
b) using the granular data to perform consumer load disaggregation ("LD")
thereby to determine usage behaviors for appliances and devices by said users;
c) collecting and compiling data relating to the behavioral patterns and
states
("BA") of the users;
d) collecting and compiling data relating to external impacts on power usage;
e) using data from one or more of a) to d), in any combination, to create an
individual consumer forecast ("CF") of power usage, said CF being collectively

aggregated across the users;
f) using data from one or more of a) to e), in any combination, to perform and

aggregate a demographic analysis ("DA")'
g) collecting (macrogrid) regional aggregate power consumption data; and
h) calculating and predicting power consumption requirements across the
macrogrid using data from one or more of a) to g)
[0024] A further aspect of the present invention comprises a method and system

for individualized smart forecasting of power usage. Such a method and system
allows a user and/or power supplier to realize and anticipate a consumption
forecast for any given utility billing period.
[0025] In one aspect, the present invention provides systems and methods for
acquiring and storing disaggregated power consumption data in a premise
without
the requirement of attaching sensors without attaching power sensors onto
every
single appliance/power draw in the premises.
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[0026] The method and system of the present invention affords many advantages
over the power "information gathering", modeling and forecasting systems
previously known. The criticality of the present innovation cannot be under-
estimated: power demand has been steadily growing over the last few decades
because of the natural expansion in both the residential and industrial
sectors.
Moreover, it is expected to steeply increase in the near future with the
integration
of Electric Vehicles (Evs). This growing demand alerts utilities, system
operators
and consumers regarding possible impacts on the electricity grid in terms of
protection, control, cost efficiency and power quality issues if proper
actions are
not taken to reduce the energy consumption and to ensure efficient monitoring.
[002711n one regard, the load disaggregation collection and analytics of the
present invention allow practical use and application of load disaggregation
data
without the requirement of attaching power sensors onto every single appliance
in
the premises.
(0028] The data analytics in accordance with the present invention yield
superior
demand forecasts by "segmenting" user profiles and modeling their consumption
behavior separately using increased input data granularity. With access to
real
time segmented data, accurate short term (and long term) demand projections
are made more accurately which affords significant cost saving to a utility
and
ultimately to a consumer, whether that consumer be a family, a business or a
manufacturing operation.
Description of the Figures
[0029]The following figures set forth embodiments in which like reference
numerals denote like parts. Embodiments are illustrated by way of example and
not by way of limitation in the accompanying figures.
[0030]Figure 1 is a schematic showing the streams and data input and usage in
accordance with the present invention;
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[0031]Figure 2 is a graph showing the Mean and Standard Deviation over Period
Length of One Day and Time-Resolution of One Hour;
[0032]Figure 3 is a graph showing Mean and Standard Deviation over Period
Length of One Week and Time-Resolution of One Day;
10033]Figure 4 is a graph showing Monthly Consumption Pattern, Demonstrating
Continuous Changes over Time;
[0034] Figure 5.a to 5.d is a series of graphs showing Daily-Hour Patterns;
[0035] Figure 6.a to 6.d is a series of graphs showing Weekly-Day Patterns;
(0036] Figure 7 is a graph showing the absence detection Fuzzy Sets;
[0037]Figure 8 is a diagram depicting a load disaggregation system based on
FHMM;
[0038] Figure 9 is an illustration for EDHMM-diff, wherein there are three HMM

chains, each used to model one appliance and wherein the final observation is
the difference between two adjacent inputs, wherein duration (d) is also
modeled;
[0039]Figure 10 is a representation of EDHMM-diff for estimation of FHMM,
which is a breakdown in detail of the box "Estimation" in Figure 8
[0040] Figure 11 is a graph illustrating EDHMM-diff detection;
[0041]Figure 12 is a pie chart depicting, at left ground truth and at right
disaggregation as calculated by one preferred LD method of the present
invention, for proportions of electricity consumption for: fridge, kitchen
outlet, bath,
light, dryer; and
[0042] Figure 13 is a pie chart depicting, at left ground truth and at right
disaggregation as calculated by one preferred LD method of the present
invention, for proportions of electricity consumption for: dryer, hot tub,
fridge,
washing machine and dish washer.
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Preferred Embodiments of the Invention
[0043]A detailed description of one or more embodiments of the invention is
provided below along with accompanying figures that illustrate the principles
of
the invention. The invention is described in connection with such embodiments,

but the invention is not limited to any embodiment. The scope of the invention
is
limited only by the claims and the invention encompasses numerous
alternatives,
modifications and equivalents. Numerous specific details are set forth in the
following description in order to provide a thorough understanding of the
invention. These details are provided for the purpose of example and the
invention may be practiced according to the claims without some or all of
these
specific details. For the purpose of clarity, technical material that is known
in the
technical fields related to the invention has not been described in detail so
that
the invention is not unnecessarily obscured.
(0044] Unless specifically stated otherwise, it is appreciated that throughout
the
description, discussions utilizing terms such as "processing" or "computing"
or
"calculating" or "determining" or "displaying" or the like, refer to the
action and
processes of a data processing system, or similar electronic computing device,

that manipulates and transforms data represented as physical (electronic)
quantities within the computer system's registers and memories into other data

similarly represented as physical quantities within the computer system
memories
or registers or other such information storage, transmission or display
devices.
[0046] The algorithms and displays with the applications described herein
are not
inherently related to any particular computer or other apparatus. Various
general-
purpose systems may be used with programs in accordance with the teachings
herein, or it may prove convenient to construct more specialized apparatus to
perform
the required machine-implemented method operations. The required structure for
a
variety Of these systems will appear from the description below. In addition,
embodiments of the present invention are not described with reference to any
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particular programming language. It will be appreciated that a variety of
programming
languages may be used to implement the teachings of embodiments of the
invention
as described herein.
[0046] An embodiment of the invention may be implemented as a method or as
a
machine readable non-transitory storage medium that stores executable
instructions
that, when executed by a data processing system, causes the system to perform
a
method. An apparatus, such as a data processing system, can also be an
embodiment of the invention. Other features of the present invention will be
apparent
from the accompanying drawings and from the detailed description which
follows.
Terms
[0047] The term "invention" and the like mean "the one or more inventions
disclosed in this application", unless expressly specified otherwise.
[0048] The terms "an aspect", "an embodiment", 'embodiment", "embodiments",
"the embodiment", "the embodiments", "one or more embodiments", "some
embodiments", "certain embodiments", "one embodiment", "another embodiment"
and
the like mean "one or more (but not all) embodiments of the disclosed
invention(s)",
unless expressly specified otherwise.
[0049] The term "variation" of an invention means an embodiment of the
invention, unless expressly specified otherwise.
[0050] The term "device" and "mobile device" refer herein interchangeably
to any
computer, microprocessing device, personal digital assistant, Smartphone other
cell
phone, tablets and the like.
[0051] A reference to "another embodiment" or "another aspect" in
describing an
embodiment does not imply that the referenced embodiment is mutually exclusive
with
another embodiment (e.g., an embodiment described before the referenced
embodiment), unless expressly specified otherwise.
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[0052] The terms "including", "comprising" and variations thereof mean
"including
but not limited to", unless expressly specified otherwise.
[0053] The terms "a", "an" and "the" mean "one or more", unless expressly
specified otherwise.
[0054] The term "plurality" means "two or more", unless expressly specified

otherwise.
[0055] The term "herein" means "in the present application", unless
expressly
specified otherwise.
[0056] The term "whereby" is used herein only to precede a clause or other
set of
words that express only the intended result, objective or consequence of
something
that is previously and explicitly recited. Thus, when the term "whereby" is
used in a
claim, the clause or other words that the term "whereby" modifies do not
establish
specific further limitations of the claim or otherwise restricts the meaning
or scope of
the claim.
[0057] The term "e.g." and like terms mean "for example", and thus does not
limit
the term or phrase it explains. For example, in a sentence "the computer sends
data
(e.g., instructions, a data structure) over the Internet", the term "e.g."
explains that
"instructions" are an example of "data" that the computer may send over the
Internet,
and also explains that "a data structure" is an example of "data" that the
computer
may send over the Internet. However, both "instructions" and "a data
structure" are
merely examples of "data", and other things besides "instructions" and "a data

structure" can be "data".
[0058] The term "respective" and like terms mean "taken individually". Thus
if two
or more things have "respective" characteristics, then each such thing has its
own
characteristic, and these characteristics can be different from each other but
need
not be. For example, the phrase "each of two machines has a respective
function"
means that the first such machine has a function and the second such machine
has a
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function as well. The function of the first machine may or may not be the same
as the
function of the second machine.
[0059] The term "i.e." and like terms mean "that is", and thus
limits the term or
phrase it explains. For example, in the sentence "the computer sends data
(i.e.,
instructions) over the Internet", the term "i.e." explains that "instructions"
are the "data"
that the computer sends over the Internet.
[0060] Any given numerical range shall include whole and fractions
of numbers
within the range. For example, the range "1 to 10" shall be interpreted to
specifically
include whole numbers between 1 and 10 (e.g., 1, 2, 3, 4, .. . 9) and non-
whole
numbers (e.g. 1.1, 1.2, . . . 1.9).
[0061] As used herein, the term "statistically significant portion
of a population"
refers to a number within any given target population which allows, from the
collection
of data and/or observance/monitoring of behaviours within said target
population, to
make inferences about that population from a given sample. In the context of
the
present invention, depending on the region and population behaviour, this can
range
from 0.1% to 10% of a given target population. It is to be appreciated that
predictability comes from gathering of enough samples to begin to observe
predictability in behaviours in the target population, and be able to identify
and
eliminate noisy from valid patterns. Target population includes a desired
subset of the
world population, for example, within a province, state, region, city, town,
borough,
district etc.....
[0062] As used herein, a "user's "state" with respect to home
energy consumption
refers to any aspect of such a user's physical character which may impact
his/her
power usage, for example, whether he/she is: at home, outside, in his/her
hometown
or away (and for how long), if home whether he/she is asleep or awake, with
house
guests..... etc....
[0063] As used herein 'status' or "state" of an appliance refers
to a state of power
usage, at a given time. For example, a lamp may have only two states: on and
off. A
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dimmer switch for a light might have a number of states of power draw. An air
conditioner may have a number of states of power draw depending on the setting

(high, medium, low, for example).
[0064] As used herein "duration" or "d" refers to the time an appliance
continues
or draws in one state before changing or being changed to another state. In an
aspect
of the method of the invention, d is not fixed knowledge but is estimated as
part of the
EDHMM-diff.
[0065] As used and as described further herein, "behavioural patterns" are
used
with the method/system of the present invention for
forecasting/disaggregation: for
example what times a user wakes up or go to bed on weekdays versus weekends,
what times user arrives home from work or school, what time user prepares
meals,
what time and for how long user, watches W, surfs the internet, does laundry,
uses
pool and pool pump, uses exercise equipment, uses medical equipment, and how
user responds to temperature changes and seasonal weather changes, etc.....
[0066] As used herein, the term "external impact" refers to one or more
signals
which are not directly measured from the user by rather from the environment
of the
user. These include, for example, weather, television schedule (including
major events
which may be broadcast at any given time), holidays, sunset/sunrise hours,
world
events etc....
[0067] As used herein "premise" refers to any building or structure or
environs
(interior or exterior) within which there are power draws, for example
appliances and
equipment. In one aspect, a premise is a residence. In another aspect, a
premise is a
commercial building or office or factory or institution.
[0068] As used herein "appliance" should be interpreted broadly to refer to
any
drawer of power within a premise, for example, a device, tools, a fixture
(including light
fixtures), an apparatus, an electrical socket etc...
Load Disaqqreoation
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[0069] As used herein, the term "load disaggregation" refers to means of
capturing and cataloguing power usage such that it can be ascribed to a
particular
power draw (for example, an appliance). Without attaching power sensors onto
every
single appliance in a home, which is expensive and cumbersome, it is
challenging to
make a correlation between the raw power usage data and total load into
individual
appliances. Load disaggregation is assessed by the proprietary methods
provided
herein and therein used to determine the energy consumption of individual
appliances
by monitoring only the power demand of the total load. One aspect of the
present
invention is the ability to accurately load disaggregate without the need for
multiple
appliance sensors.
[0070] As used herein, "power draw" refers to both power draw and/or energy

consumption.
[0071] In another aspect, the LD data acquired thereby is applied to the
methods
and systems of power modeling and forecasting.
[0072] There are a variety of LD methods in the literature which attempt to

estimate a breakdown of consuming appliances, in real-time or otherwise. Such
algorithms may require superior hardware (e.g., higher sampling rates),
sophisticated
algorithms, a thorough database of all device pattern signatures, and an
adequate
computing platform. Alternative methods use specialized hardware, such as
"smart
plugs,' to be installed on each appliance so that each appliance's consumption
can be
measured separately.
[0073] It is to be understood that within the method and system of modeling
(to
generate forecasts), a number of such LD methods may be used, some employing
smart meters and/or appliance specific sensors and others not. As such, the
method
and system of modeling is not limited to any one mode of acquiring the LD
data.
Preferably, any such method of acquiring LD data does not require appliance
user
involvement or interaction. In other words, manual LD techniques are not
preferred.
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Most preferred are such methods which continually monitor a premises power
profile
feeding such data into the method as described herein.
[0074] In essence, one aspect of the present invention provides systems and

methods of providing granular consumption information and load disaggregation
data
to users on "computing platforms" (including, but not limited to, mobile
devices such
as smartphones, tablets, netbooks and laptops, as well as non-mobile personal
computers).The granular consumption information described herein can be
generated
by for example, smart grid networks, or by custom electric consumption sensors
(e.g.
current sensors, etc.). There are also numerous ways to communicate the
generated
data to the end user (including a power provider). For example, this includes
the use
of the Internet, regional wireless communication methods, cellular networks,
home
networks such as Wi-Fi, broadband, Zigbee, etc...
[0075] Within the scope of this invention, modeling uses "historic
information" to
create an understanding of how a power using system works, and that
understanding
is then used to forecast or predict how the power using system will function
in future.
In creating this model, a given population's consumption behaviors are used to
create
a granular forecast of future consumption. To model consumption behaviors, one

aspect of the present invention provides that LD data (how a user uses power
drawing
devices) is 1) collected and 2) analyzed.
Factorial Hidden Markov Model (FHMM)
[0076] One preferred method of performing LD, and acquiring data therefrom
is
through FHMM. This method provides means for non-intrusive LD based on power
consumption data collected from standard smart meters in premises with a low
sampling rate (for example, 1 read per minute, or 1 read per 15 minutes) and
wherein
LD is determined in an unsupervised way, without the need to measure the
"ground
truth" to calibrate the system for every premise (for example, within a
macrogrid).
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[0077] In a preferred mode, to cope with the complex scenarios
offered in real
world applications, the present method uses a "detect-and-tune" step to
estimate the
FHMM model separately for different potential appliances. In addition, there
is
provided a quadratic programming (QP) based optimization method for the
inference
of FHMM, in order to increase the efficiency of the methodology in real-time
applications.
[0078] A Hidden Markov Model (HMM) is a discrete-time stochastic
process
including an underlying finite-state Markov Chain (state sequence) and a
sequence of
random variables whose distributions depend on the state sequence only through
the
current state (observation sequence). The state sequence is not observable,
and
hence conclusions about the process must be made using only the observation
sequence.
[0079] HMMs are among the most successful models for dealing with
time series
data and the machine learning community has extended this model in many
orthogonal directions. The FHMM is an extension of the classical hidden Markov

model in which the hidden state is factored2. In other words, the information
from the
past is propagated in a distributed way through a set of parallel Markov
chains. This
model has been used in vision, audio processing and natural language
processing.
[0080] So, HMM have been used to model processes in which some
variables are
hidden, but are assumed to be statistically related to observed variables. The
HMM
makes certain assumptions, including that the values of the hidden variables
(states)
depend only upon previous values of the hidden variables, that the value of
each
hidden variable is independent of the values of the other hidden variables,
and that
2
Ghahramani, Z., &Jordan, M. I. (1997). Factorial hidden markov models. Machine
Learning, 29, 245(273).
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the values of the observed variables depend only on the current values of the
hidden
variables. Under these assumptions, a time sequence of values of the hidden
variables is inferred from the temporal variation of the observed variable
values and
knowledge of the parameters of the stochastic process relating the observed
variables
to the hidden ones.
[0081] By definition: an HMM is a collection or random variables consisting
of a set
of T discrete scalar variables X1 :T and a set of T other variables Yl:T which
may be
either discrete or continuous (and either scalar - or vector-valued). These
variables,
collectively, possess conditional independence properties.
[0082] In FHMM, exact inference has a complexity which is exponential in
the
number of hidden dynamics, and approximate inference techniques are generally
required. Existing FHMM implementations generally operate on observed
variables
that are continuous. For example, the variational inference framework of
Ghahramani2
is limited to continuous (Gaussian) observation variables. The hidden states,
on the
other hand, are assumed to be discrete, and the number of possible states for
a given
hidden dynamic is an input parameter to the FHMM analysis.
[0083] What has been discovered is that FHMM is a powerful model that is a
perfect choice to model the complicated LD challenge. However, the estimation
and
inference for this FHMM model is NP hard and before the present solution,
described
in detail herein, no solutions were provided. Although there are several
approximating
methods for these purposes, they require either annotation of signals by human

experts, or very complicated Monte Carlo Markov Chain (MCMC) sampling, which
are
usually extremely slow. To overcome this challenge within the context of the
present
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invention, there is provided a method of estimating each of the FHMM chains
(each
representing a power draw within a premise) by using Explicit Duration Markov
Model
on Difference (EDHMM-diff) of Signals.
[0084] Preferably, signal segments (used interchangeably with the term
"signal
clips") for estimation of the Explicit Duration Markov Model are mined from a
testing
power signal by a "detect-and-tune" method, wherein a database, store or
library of
appliance models is used to detect sub-windows which contain signals from a
"like"
type of appliance within a premise, and thereafter these sub-windows of
signals are
used to tune the appliance models from the database, store or library to "fit"
the
appliances in this selected premises. Through this "detect-and-tune" approach,
it is
possible to successfully estimate all the hidden Markov chains without
measuring
training data from the premise being tested.
[0085] In a further aspect of the present invention, there is provided a
database,
store or library of appliance models, and the power signal generated by each
such
appliance. Such a database is created by a) building a sensor network in a
plurality of
premises (each comprising common appliances); b) monitoring over a selected
time
all the common appliances to collect get sufficient real data in relation to
the power
signal generated by each such appliance (power signal data); and c) storing
said
power signal data.
Premise of LD System
[0086] For a premise, the power signal obtained from a smart meter is the
summation of the power consumption of all power draws or working appliances in
this
premise. Assuming the appliances work independently, it is possible to use one
I-/MM
to model the behavior of one power draw or appliance. The final observation is
the
summation of the outputs of these HMMs. The statistical model to describe such

system is the FHMM.
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[0087] In a perfect world, one could estimate a FHMM perfectly and then use
it to
disaggregate power signals into different appliances as depicted in Figure 8.
EDHMM-diff for Estimation
[0088] In reality, however, the estimation (estimating the parameters of
FHMM)
and inference (disaggregating the final observation into different HMM chains)
are not
traceable (it is actually NP hard and cannot be solved by any existing
computing
machine). Researchers have proposed an inexact optimization algorithm to do
the
inference of FHMM3, presently applied within the method described herein. As
such,
the present method provides a means to disaggregate the total power signals
provided the correct parameters of the FHMM (wherein such parameters come from

estimation). Within the preferred method of the invention, using a "detect-and-
tune"
step, each of the liMM of the FHMM are estimated independently. Preferably,
the
model applied within the method of the present invention for estimation
purpose is
EDHMM-diff, as illustrated in Figure 9.
[0089] Figure 9 shows that each of the independent HMM chains 30, 32 and 34
is
each used to model one "putative" appliance (36, 38, and 40) and the final
observation
is the difference between two adjacent outputs. Assumption is made that the
probability for more than two appliances changing states together is very
small, and
as such, the difference observation (delta y_t) contains the information of
status
change for at most one of these HMMs. In addition, the method provides that
duration
d for the status of each HMM is modeled. Together, this model is called
Explicit
Duration HMM with Difference Observations and is used for LD.
[0090] Within the scope of the invention, to use EDHMM-diff to perform
estimation
for FHMM, there is created, collected and stored a database or library of
models of
3 Zico Kolter and TOMMI Jaakkola. Approximate Inference in Additive Factorial
HMMs with Application to Energy Disaggregation.
AISTATS, 2012
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usual appliances in the premises. This database or library is used as a
template
(using EDHMM-diff), to detect the signal clips that contain the power signal
generated
by a given appliance. The signal clips are then used to fine-tune the FHMM
parameters using EDHMM-diff. These method steps comprises the "detect-and-
tune"
steps, as described above and provided in Figure 10.
[0091] So, within the present invention there is provided the use of an
FHMM
model for LD purpose and a method for estimating FHMM using, most preferably,
an
EDHMM-diff. In Figure 11, the light blue signal is the aggregated power signal

obtained from smart meter, and the deep blue windows are the signal clips,
which
contain power signal generated by cloth dryers, and the red windows are clips
that
don't contain signals from dryer. This detection is done by EDHMM-diff, and no
other
previous methods (HMM, explicit duration HMM, etc.) could achieve this goal.
[0092] It is to be understood that one or both of energy consumption of an
appliance and power draw signals of an appliance may be used for the LD
methodology. "Power draw" refers to watts, whereas "energy" refers to watt-
hours,
and both such signals may be used for LD, as provided herein.
[0093] Figures 12 and 13 are pie charts illustrating the accuracy of the
FHMM
estimated parameters and illustrate the proportions of electricity
consumptions of each
device, with the left charts in each figure being the ground truth, and the
right charts in
each figure representing results achieved by the presently disclosed LD method
and
system. For example, in Figure 12, the ground truth is that the kitchen outlet
has a 9%
proportional power draw. The disaggregation results show the same 9%
proportional
power draw.
[0094] The exemplary embodiment relates to a system and method in which
statistical analysis of discrete observations provides useful information
about
underlying states of the system of interest (appliance power draws). In
various
aspects, the FHMM is used to model the system over time. The method employs
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inference and learning algorithms which allow the method to be scaled up, as
required.
[0095] With all this in mind, the present invention further provides, in
another
aspect, a method of disaggregating a power load, at a premises, said premises
comprising at least two appliances, the method carried out by computer program
code
stored on non-transient computer-readable medium and executed by a processor,
said method comprising at least the following steps:
a) devise a model power draw of a first appliances using HMM (first power
draw);
b) devise a model power draw of a second appliance using HMM (second power
draw);
c) calculate the difference between the total power draw (first power draw 4-
second
power draw) at time t and at time t-1;
d) estimate FHMM using EDHMM-diff, wherein a database comprises power signal
clips
generated by appliances, including those appliances which are the same as or
comparable to the first appliance and the second appliance; and
e) update the parameters of FHMM based on the estimation (at step d) using
EDHMM-
diff.
Forecasting
[0096] Referring now to Figure 1, the top bar identifies the major
components of
the forecasting method and system (generally at 10) referred to herein as the
Granular
Load Projection ("GLP"). Sources of information and data that GLP system
collects,
compiles and analyzes are identified as:
1. Individual user consumption data, 12: which data contains real time
consumption information for a sample number of individual residential,
commercial and/or industrial users. The sample size must be statistically
significant, given the total population of the region.
2. External Inputs (referred to also as external signals) 14 which are those
inputs that affect user consumption or could be used to predict consumption.
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These include, but are not limited to weather forecasts and patterns (as well
as history), TV programming, local events, holidays, etc...
3. Regional Aggregate Consumption 16: which is the total electricity
consumption in a given macrogrid region, including historic data, current
data,
and/or forecast. The data resolution can range, for example, from a fraction
of
a second to a number of hours or beyond. It is to be clearly understood that
the present invention is not limited to any particular time range.
4. Deferring investments: The LD and forecasting analytics described herein
may be used for a variety of peak reduction strategies such as designing
demand response programs or consumer educational campaigns. Peak
reduction results in more available capacity, which could defer investments in

infrastructure and extend the life cycle of assets
[0097]Using the above information, GLP creates a forecast of the total
consumption (18) in the macrogrid region over a selected future time period,
whether that is hours, days, or months. As used herein in regards to data
collection, "real time" is selected from a parameter of within a five minute
interval
or less or within a one hour interval or less hence providing the benefit of
the
highly granular data. It is to be clearly understood, however, that real or
near-real
time may most preferably mean herein a level of approximately 5 minutes or
less
however other, longer times may also be used in certain situations, such
longer
times being over hour intervals. In certain embodiments, speed may only be
constrained by the speed of processors and/or network connections and
communication conduits.
[0098]The forecasting and predicting method and system of future power
requirements, as provided herein is for the purpose of at least one the
following,
which comprises an non-exhaustive list:
= decreasing and tracking consumer usage of the power resource
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= decreasing consumer costs for power
= maximizing revenue for the power provider,
= minimizing consumer discomfort, and identifying future peak usage times
and
requirements
= optimizing power distribution: utilities have limited bandwidth to send
power
from one location to another, so by knowing exactly how much each region is
going to require power, they can optimize their distribution planning and make

best use of their available bandwidth
= .prioritizing infrastructure upgrades: data can be used to decide which
regions
require future upgrades in infrastructure (distribution lines, power
generation,
etc....)
(009910ne key aspect of the method and system of the present invention is the
means to collect, analyze and manipulate the aforementioned data from
information sources 1) to 3) above. GLP creates valuable and usable
forecasting
data from a statistically significant sample of a user population. To do so,
the
method within the scope of the present invention comprises:
a) receiving and compiling by a processor in real-time granular power
consumption data from a statistically significant portion of a population of
power
users (the "users") within the macrogrid (the "granular data");
b) using the granular data and computer processor to perform consumer load
disaggregation ("LD") thereby to determine usage behaviors for appliances and
devices by said users (20);
c) collecting and compiling data relating to the behavioral patterns and
states
("BA") of the users (22);
d) collecting and compiling data relating to external impacts (14) on power
usage;
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e) using data from one or more of a) to d), in any combination, to create by a

processor, an individual consumer forecast ("CF") of power usage (24), said CF

being collectively aggregated across the users;
f) using data from one or more of a) to e), in any combination, to perform and

aggregate by a processor a demographic analysis ("DA"), shown at 26.'
g) collecting at 28 (macrogrid) regional aggregate power consumption data; and
h) calculating and predicting power consumption requirements across the
macrogrid using data from one or more of a) to g)
[00100] In one embodiment, external impacts are selected from
the group
consisting of weather patterns and predictions, calendar month, economic
indicators, world events, television programming, equipment age and
maintenance, and population mobility. Numerous available external signals
(e.g.,
TV programming) are not often used for load forecasting since regional
consumption data alone does not provide enough information to model the
response. However, oranular data enables load modeling based on such
variables since details of user consumptions are made visible, and thus method

and system of the present invention is capable of utilizing a wider array of
external signals than the conventional forecast methods
[00101] In one embodiment, BA includes data related to user
behavioral
states selected from the group consisting of sleeping, identification of when
woken, when on vacation, when working from home, charging electric vehicles,
and when user might be on an extended absence. In one embodiment, BA
includes data related to user behavioral patterns selected from the group
consisting of sleeping hours, weekend schedules, weekday schedules, departure
to work and arrival home from work, amount of time spent away from home.
[00102] By performing LD at a premises level using the
granular data, the
usage behaviors for major appliances are observed. This improves the load
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modeling accuracy with respect to a variety of input signals. For instance,
the load
demand by AC/heaters can be directly observed in response to weather changes.
[00103] By observing, collecting and analyzing real-time granular
consumption data BA, GLR identifies consumer behaviors that are valuable to
forecasting. This includes user's behavioral states (e.g., sleeping, just-
woken,
away, on-vacation, etc.) or their behavioral patterns (e.g., sleeping hours,
weekend vs. weekday schedules, etc.). Such behaviors are often unobservable
through external conventionally-available means. The BA results are used to
increase forecasting accuracy. More examples of behavioral information and
data
extracted using BA include:
= Whether an Electric Vehicle (EV) is being charged; as well as the EV
usage
pattern.
= The user has just woken up
= The user has just arrived home
= The user's weekend vs. weekday schedules
= Whether the customer is on vacation
[00104] Using the real-time granular consumption data, external signals,
LD, and BA, the consumption by individual users is forecasted. The individual
forecasts from the sample population can then be aggregated and interpolated
to
form bases of the total load demand projection. While the individual consumer
forecast is used for forecasting aggregate regional consumption, it can also
be
presented to users as a separate product offering. Consumer forecast can help
individuals better understand their usage behaviors, and meet their target
conservation objectives.
[00105] As noted above, using the LD, BA and CF data and analysis, a
demographic analysis (DA) can be performed. DA aggregates the details of
Consumer behaviors observed, to create template models and projections for
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different demographic categories. It helps identify the best categorization
criteria
that produce user classes with higher predictability value. DA is particularly

important when interpolating the load forecast from a statistical sample of
the
population to estimate the total regional load demand. Demographic analytics
aggregate the details of consumer behaviors obtained using BA and LD, to
create
template models and projections for different demographic categories.
[00106] Demographic analysis takes into account that not all
influencing
factors will impact all consumers' energy usage the same way. The demographic
analysis is particularly important when interpolating the load forecast from a

statistical sample of the population to estimate the total regional load
demand
[00107] Using the above information, GLR creates a forecast of the
total
consumption (18) in the region over a selected future time period, whether
that is
hours, days, months, quarters, seasons or years. As used herein, "real time"
refers to highly granular data collection at within a one hour interval or
less, more
preferably with a five minute interval or less
[00108] One aspect of the novelty of the GLR system is in taking
advantage
of the key pieces of data collection, compilation and analysis based not on
users
within a microgrid population but on a statistically meaningful sample of
individual
user's consumption data and then, using a computer processor, extrapolating
such data to yield macrogrid level projections. So a forecast for an entire
macrogrid can be created based on a statistically significant sample
population.
Microgrid data cannot be extrapolated in this manner.
[00109] Within the scope of the present invention a population sample
has
a statistical significance if its size is larger than or equal to the smallest
possible
sample, using which the patterns of the entire population can be observed and
modeled within required accuracy. In practice, statistically significance
sample
sizes could be as small as 0.1%, while 10% sample size or larger could also be

used. One skilled in the art may define what confidence level is desired and
what
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margin of error is acceptable. A confidence level of 95% and an error margin
of
5% tell you that your result will be within 5% of the true answer 95% of the
time
you run the survey. So 11 100 samples were tested, 95 of them would return a
result that was within 5% of the truth.
[00110] Generally, a
correct sample size is a function of three elements¨

the sub-universe (i.e. how many people make up the group whose behavior you
are trying to represent), your desired error margin, and your preferred
confidence
level.
[00111] Data
Acquisition: New smart meter technology is rapidly being
introduced to the industry to facilitate time-of-use metering at residences,
permitting utilities to charge for electrical usage dependent upon the time of
use
and for consumers to take advantage of times at which a lower cost is assessed

to the use of electricity.
[00112] In one aspect,
the data acquiring means of the present invention
comprises one or more network managers. Preferably, these one or more
network managers which each comprise a modem capable of transmitting
measurement data over a network. In one aspect, the system additionally
comprises one or more network managers which relay data from the sensor
nodes to a server via one or more digital communication channels including but

not limited to broadband, DSL, cellular, satellite, WiMAX and Wifi. In
particular, it
is noted that some Smartmeters communicate over wired channels, and some
utilities include internet-based data collection systems to increase their
data
sampling rate. The present invention incorporates all such data collection
means.
[00113] In one aspect,
the system additionally comprises one or more
network managers which aggregate and relay the data from the database storage
to a server and wherein said server enables viewing of the data by a viewer
via
an interface. In one aspect, the system additionally comprises one or more
network managers which aggregate and relay the data from a data storage
system to a server and wherein said server enables viewing of the data by a
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viewer via an interface and wherein said interface is selected from the group
consisting of a desktop computer, a laptop computer, a hand-held
microprocessing device, a tablet, a Smartphone, iPhone , iPad , PlayBook and
an Android device. Those skilled in the relevant art will appreciate that the

invention can be practiced with many computer configurations, including hand-
held devices, multiprocessor systems, microprocessor-based or programmable
consumer electronics, personal computers ("PCs"), network PCs, mini-computers,

mainframe computers, and the like. In one aspect, the measurement data is
communicated wirelessly on a peer-to-peer network to a central network
manager. In one aspect, the measurement data is collected in situ from network

managers or sensors including but not limited to current monitoring sensors
that
are used to estimate power consumption. This can be achieved by workers on
site either on the ground or using a bucket truck. In one aspect, the system
comprises more than three sensor nodes. In one aspect, the system may be
temporarily field deployable on one or more supply line electrical wires and
then
moved and reset on other supply line electrical wires without the requirement
of
any wire splicing for such deployment and re-deployment.
(001141 A utility's (such as BC Hydro's) Smart Grid network may, in one
embodiment, be preferably be used to gather data for use within the method and

system of the present invention. In the alternative, other systems may be used
to
monitor individual power consumption. In a most preferred of such forms, the
field
deployable node includes one or more components including, but not limited to,
a
clamp-on current sensor, a micro controller and an RF module. The nodes
communicate with each other to self-form into a mesh, star, or tree network
topology forming a Field Area Network (FAN). The power usage information from
each device is then relayed through said network, and sent to the utility to
be
compared to other usage data. The system is field deployable requiring no
splicing into the electrical wire to allow for quick setup and extraction of
the
system to allow movement of said system to another location.
Computer Processing
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[00115] Within the
scope of the present invention, data acquisition,
compilation, and analysis may preferably be controlled by a computer or
microprocessor. As such, the invention can be implemented in numerous ways,
including as a process, an apparatus, a system, a computer readable medium
such as a computer readable storage medium or a computer network wherein
program instructions are sent over optical or communication links. In this
specification, these implementations, or any other form that the invention may

take, may be referred to as systems or techniques. A component such as a
processor or a memory described as being configured to perform a task includes

both a general component that is temporarily configured to perform the task at
a
given time or a specific component that is manufactured to perform the task.
In
general, the order of the steps of disclosed processes may be altered within
the
scope of the invention.
[00116] The following
discussion provides a brief and general description of
a suitable computing environment in which various embodiments of the system
may be implemented. In particular, this is germane to the network managers,
which aggregate measurement data and downstream to the servers which
enables viewing of the data by a user at an interface.
[00117] Although not
required, embodiments will be described in the
general context of computer-executable instructions, such as program
applications, modules, objects or macros being executed by a computer. Those
skilled in the relevant art will appreciate that the invention can be
practiced with
other computer configurations, including hand-held devices, multiprocessor
systems, microprocessor-based or programmable consumer electronics, personal
computers ("PCs"), network PCs, mini-computers, mainframe computers, and the
like. The embodiments can be practiced in distributed computing environments
where tasks or modules are performed by remote processing devices, which are
linked through a communications network. In a
distributed computing
environment, program modules may be located in both local and remote memory
storage devices.
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[00118] A computer system may be used as a server including one or more
processing units, system memories, and system buses that couple various
system components including system memory to a processing unit. Computers
will at times be referred to in the singular herein, but this is not intended
to limit
the application to a single computing system since in typical embodiments,
there
will be more than one computing system or other device involved. Other
computer
systems may be employed, such as conventional and personal computers, where
the size or scale of the system allows. The processing unit may be any logic
processing unit, such as one or more central processing units ("CPUs"),
digital
signal processors (''DSPs"), application-specific integrated circuits
("ASICs''), etc.
Unless described otherwise, the construction and operation of the various
components are of conventional design. As a result, such components need not
be described in further detail herein, as they will be understood by those
skilled in
the relevant art.
[00119] A computer system includes a bus, and can employ any known bus
structures or architectures, including a memory bus with memory controller, a
peripheral bus, and a local bus. The computer system memory may include read-
only memory ("ROM") and random access memory ("RAM"). A basic input/output
system ("BIOS"), which can form part of the ROM, contains basic routines that
help transfer information between elements within the computing system, such
as
during startup.
[00120] The computer system also includes non-volatile memory. The non-
volatile memory may take a variety of forms, for example a hard disk drive for

reading from and writing to a hard disk, and an optical disk drive and a
magnetic
disk drive for reading from and writing to removable optical disks and
magnetic
disks, respectively. The optical disk can be a CD-ROM, while the magnetic disk

can be a magnetic floppy disk or diskette. The hard disk drive, optical disk
drive
and magnetic disk drive communicate with the processing unit via the system
bus. The hard disk drive, optical disk drive and magnetic disk drive may
include
appropriate interfaces or controllers coupled between such drives and the
system
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bus, as is known by those skilled in the relevant art. The drives, and their
associated computer-readable media, provide non-volatile storage of computer
readable instructions, data structures, program modules and other data for the

computing system. Although a computing system may employ hard disks, optical
disks and/or magnetic disks, those skilled in the relevant art will appreciate
that
other types of non-volatile computer-readable media that can store data
accessible by a computer system may be employed, such a magnetic cassettes,
flash memory cards, digital video disks ("DVD"), Bernoulli cartridges, RAMs,
ROMs, smart cards, etc.
[00121] Various program modules or application programs and/or data can
be stored in the computer memory. For example, the system memory may store
an operating system, end user application interfaces, server applications, and
one
or more application program interfaces ("APIs").
[00122] The computer system memory also includes one or more
networking applications, for example a Web server application and/or Web
client
or browser application for permitting the computer to exchange data with
sources
via the Internet, corporate Intranets, or other networks as described below,
as
well as with other server applications on server computers such as those
further
discussed below. The networking application in the preferred embodiment is
markup language based, such as hypertext markup language ("HTML"),
extensible markup language (''XML") or wireless markup language ("WML"), and
operates with markup languages that use syntactically delimited characters
added
to the data of a document to represent the structure of the document. A number
of
Web server applications and Web client or browser applications are
commercially
available, such those available from Mozilla and Microsoft.
[00123] The operating system and various applications/modules and/or
data can be stored on the hard disk of the hard disk drive, the optical disk
of the
optical disk drive and/or the magnetic disk of the magnetic disk drive.
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[00124] A
computer system can operate in a networked environment using
logical connections to one or more client computers and/or one or more
database
systems, such as one or more remote computers or networks. A computer may
be logically connected to one or more client computers and/or database systems

under any known method of permitting computers to communicate, for example
through a network such as a local area network ("LAN") and/or a wide area
network ("WAN") including, for example, the Internet. Such
networking
environments are well known including wired and wireless enterprise-wide
computer networks, intranets, extranets, and the Internet. Other embodiments
include other types of communication networks such as telecommunications
networks, cellular networks, paging networks, and other mobile networks. The
information sent or received via the communications channel may, or may not be

encrypted. When used in a LAN networking environment, a computer is
connected to the LAN through an adapter or network interface card
(communicatively linked to the system bus). When used in a WAN networking
environment, a computer may include an interface and modem or other device,
such as a network interface card, for establishing communications over the
WAN/Internet.
[00125] In a
networked environment, program modules, application
programs, or data, or portions thereof, can be stored in a computer for
provision
to the networked computers. In one embodiment, the computer is
communicatively linked through a network with TCP/IP middle layer network
protocols; however, other similar network protocol layers are used in other
embodiments, such as user datagram protocol ("UDP"). Those skilled in the
relevant art will readily recognize that these network connections are only
some
examples of establishing communications links between computers, and other
links may be used, including wireless links.
[00126] While in
most instances a computer will operate automatically,
where an end user application interface is provided, a user can enter commands

and information into the computer through a user application interface
including
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input devices, such as a keyboard, and a pointing device, such as a mouse.
Other input devices can include a microphone, joystick, scanner, etc. These
and
other input devices are connected to the processing unit through the user
application interface, such as a serial port interface that couples to the
system
bus, although other interfaces, such as a parallel port, a game port, or a
wireless
interface, or a universal serial bus ("USB") can be used. A monitor or other
display device is coupled to the bus via a video interface, such as a video
adapter
(not shown). The computer can include other output devices, such as speakers,
printers, etc
[00127] In a preferred form, it is envisioned that data relating to the
present
disclosure can be transmitted over such networks or connections. The computer
system can be adapted to communicate with a user/participant and/or a device
used by a user/participant. The computer system is adaptable to communicate
with other computers over the Internet, or with computers via a server. Each
computing device (including mobile devices) includes an operating system (OS),

which is software, that consists of software programs and data that runs on
the
devices, manages the device hardware resources, and provides common
services for execution of various application software. The operating system
enables an application program to run on the device.
[00128] In such way, as will be appreciated by those skilled in the art,
a
computer readable medium stores computer data, which data can include
computer program code that is executable by a computer, in machine readable
form. By way of example, and not limitation, a computer readable medium may
comprise computer readable storage media, for tangible or fixed storage of
data,
or communication media for transient interpretation of code-containing
signals.
Computer readable storage media, as used herein, refers to physical or
tangible
storage (as opposed to signals) and includes without limitation volatile and
non-
volatile, removable and non-removable storage media implemented in any
method or technology for the tangible storage of information such as computer-
readable instructions, data structures, program modules or other data.
Computer
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readable storage media includes, but is not limited to, RAM, ROM, EPROM,
EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD,
or other optical storage, magnetic cassettes, magnetic tape, magnetic disk
storage or other magnetic storage devices, or any other physical or material
medium which can be used to tangibly store the desired information or data or
instructions and which can be accessed by a computer or processor.
[00129] The method of the present invention, for example, the analytics
(including the signal clip detection) may occur on: a computer or a set of
computers at the utility site, at a third party company location, one or more
servers in a cloud, or even on-site at the premise (for example household)
where
the data is collected. In respect to the latter, the algorithm required to
implement
the method may be placed on a Network Interface Card (NIC) on a smart meter at

the premise. NICs are responsible for communicating smart meter measurements
to the outside world i.e. outside the premise, but they also have other
processing
capabilities on-board. So, in one aspect of the present invention, rather than

conveying raw power data away from the premise, for application to the method
and processing of the invention "off site", in one preferred aspect, all such
processing is implemented by embedded programs on a NIC card at a premise.
[00130] In regards to another aspect of the invention, wherein analytics
are
implemented on an Internet server, a method is provided as follows: every time
a
power/energy reading is measured by a sensor, the data is communicated to the
server, which hosts a database. The database stores a copy of the data. Either

immediately, or at a future time interval, when the results of LD/analytics
are
needed (this could be frequent 'interval' runs of the analytics, or just on-
demand
runs whenever a user wants to see their breakdown), the data is then passed
into
the analytics processing engine, which is also in a server (for example, this
may
be on the same server as the database, or a different server; as long as they
are
both online, enabling communication therebetween. The new readings along with
a window of historic measurements are then fed to the analytics engine to
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perform operations such as forecasting, FHMM LD, etc.. all in accordance with
the methods described herein.
[00131] Although it is described herein that a server may be a cloud
server
(one or a set of server computers hosted off-site), it is worth noting that
some
jurisdictions may have regulations that limit the data leaving a
region/country
etc..., for example, user consumption data collected in Australia may not
leave
Australia. As a solution, one may utilize a cloud server provider, such as
Amazon,
that has an Australian server base, or one may set up our private servers in
that
country or even inside the utility premises.
[00132] In a preferred aspect, the signals are detected on the same
computer processor/machine that performs the remainder of the analytics.
[00133] Some aspects of the present invention may be delivered via mobile

application. In this way, a user launches an app created by an app creator and

downloaded to the user's mobile device to view digital content items and can
connect to a front end server via a network, which is typically the Internet,
but can
also be any network, including but not limited to any combination of a LAN, a
MAN, a WAN, a mobile, wired or wireless network, a private network, or a
virtual
private network. As will be understood a very large numbers (e.g., millions)
of
users are supported and can be in communication with the website via an app at

any time. The user may include a variety of different computing devices
[00134] Application delivery platform can be implemented entirely in
hardware and/or a combination of hardware and/or software in execution.
Further,
application delivery platform can be incorporated within and/or associated
with
other compatible components. Additionally, application delivery platform can
be,
but is not limited to, any type of machine that includes a processor and/or is

capable of effective communication with network topology and/or cloud.
Illustrative machines that can comprise application delivery platform can
include
desktop computers, server class computing devices, laptop computers, notebook
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computers, Tablet PCs, consumer and/or industrial devices and/or appliances,
hand-held devices, and the like.
[00135] Network topology and/or cloud can include any viable
communication and/or broadcast technology, for example, wired and/or wireless
modalities and/or technologies can be utilized to effectuate the claimed
subject
matter. Moreover, network topology and/or cloud 104 can include utilization of

Personal Area Networks (PANs), Local Area Networks (LANs), Campus Area
Networks (CANs), Metropolitan Area Networks (MANs), extranets, intranets, the
Internet, Wide Area Networks (WANs)--both centralized and/or distributed--
and/or
any combination, permutation, and/or aggregation thereof. Furthermore, as
those
skilled in the art will appreciate and understand various data communications
protocols (e.g., TCP/IP, Ethernet, Asynchronous Transfer Mode (ATM), Fiber
Distributed Data Interface (FDDI), Fibre Channel, Fast Ethernet, Gigabit
Ethernet,
Wi-Fi, Token Ring, Frame Relay, etc.) can be utilized to implement suitable
data
communications.
[00136] Additionally application delivery server/platform may
include a
provisioning component that, based at least in part on input received from a
portal
component, can automatically configure and/or provision the various disparate
mobile devices with appropriate applications.
[00137] The foregoing detailed description has set forth
various
embodiments of the devices and/or processes via the use of examples. Insofar
as such examples contain one or more functions and/or operations, it will be
understood by those skilled in the art that each function and/or operation
within
such examples can be implemented, individually and/or collectively, by a wide
range of hardware, software, firmware, or virtually any combination thereof.
In
one embodiment, the present subject matter may be implemented via ASICs.
However, those skilled in the art will recognize that the embodiments
disclosed
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herein, in whole or in part, can be equivalently implemented in standard
integrated circuits, as one or more computer programs running on one or more
computers (e.g., as one or more programs running on one or more computer
systems), as one or more programs running on one or more controllers (e.g.,
microcontrollers) as one or more programs running on one or more processors
(e.g., microprocessors), as firmware, or as virtually any combination thereof,
and
that designing the circuitry and/or writing the code for the software and or
firmware would be well within the skill of one of ordinary skill in the art in
light of
this disclosure.
[00138] In addition, those skilled in the art will appreciate that the
mechanisms taught herein are capable of being distributed as a program product

in a variety of forms, and that an illustrative embodiment applies equally
regardless of the particular type of signal bearing media used to actually
carry out
the distribution. Examples of signal bearing media include, but are not
limited to,
the following: recordable type media such as floppy disks, hard disk drives,
CD
ROMs, digital tape, flash drives and computer memory; and transmission type
media such as digital and analog communication links using TDM or IP based
communication links (e.g., packet links).
Individual Consumer Forecasts (CF)
[00139] The present invention is not solely about the creation of
regional
consumption forecasts. Using the data collection and analyses herein defined,
and as prescribed in the methods and systems of this aspect of the invention,
individualized smart budgeting can also be achieved. So, the present invention

further comprises a budgeting method and system which allows each user to
specify a target budget for their billing period. The analysis provides users
with
real-time feedback as to whether their consumption habits are likely to meet
their
desired budget.
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[00140] The simplest way to approach budgeting is to divide the total
budget by the number of hours in the billing cycle, and inform the user when
their
hourly consumption goes beyond the pre-defined hourly budget. However, this
method would not provide users with much beneficial feedback because the users

require different amounts of electricity at different hours and days. For
instance, if
the amount of allocated budget for 5PM is the same as 5AM, the user will
always
appear to be over-consuming (i.e., consumption > budget) at 5PM, and under-
consuming (i.e., consumption < budget) at 5AM.
[00141] The Smart Budgeting (SB) method and system as described
herein, on the other hand, provides users with a more intelligent and
practical
feedback. At each hour, the allocated budget is determined using the following

variables:
a) How much of the budget is left to be consumed (subtract the money spent so
far in the billing period, from the total budget).
b) The forecasted consumption for this day and hour.
c) The total forecasted consumption in the remaining portion of the billing
cycle.
d) The observed deviation in user's consumption for the current day and hour.
[00142] The Smart Budgeting system and method of the present invention is
capable of taking into account the fact that hours with higher consumption
amount and
higher consumption deviation represent better opportunities for users to
conserve
energy.
[00143] In operation, Smart Budgeting in accordance with the present
invention
may be illustrated (by way of example) as follows:
[00144] The given data for analysis:
= The closing date of the billing cycle
= Hourly readings of user's consumption over the previous months
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Performance Evaluation
[00145] Running the algorithm over user's previous months of
consumption,
the algorithm performance can be measured by comparing the forecast value to
the actual billing cost of the corresponding period.
BR =ZCi, BF =1Fi
iEp iEP
ep = 1Bp BR1 Ci)
iEP
where C is the hourly consumption, F is the hourly forecast, BR is the real
billing cost, BF
is the forecasted bill, P is the billing period, and Ep is the forecast error
of the given
period.
[00146] Choosing different billing cycle closing dates would result in

different error values. Herein provided is a performance evaluation method in
which the outcome depends only on the forecast algorithm itself, and not the
billing period. Hence, the present method uses the above method over all
possible billing periods (i.e. starting at every single day in the entire
data):
P1= ep
VPOC)
where PI is the Performance Index. The lower the PI, the more accurate the
forecast
algorithm.
[00147] The PI can be calculated for all available load profiles.
Whether
designing, improving or comparing forecast algorithms, the ultimate intention
is to
minimize PI which in turns leads to more accurate forecast bills.
[00148] PI can be obtained for different billing cycle lengths (e.g. a
week
long, a month long, or a two month billing cycle). In general, it has been
observed
that as the billing cycle grows the P1 increases exponentially.
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[00149] It is worth noting that the forecast made at the beginning of a
two-
month billing period is basically the worst case scenario and it is likely to
create
the most inaccurate result. As the time moves forward, the length of the
period
over which we forecast shrinks, and the length of the time for which actual
readings are used grows. Consequently, by the end of the billing period, the
value presented as "forecast bill" consists mostly of actual readings rather
than
forecast values. Therefore, the accuracy increases as the time passes.
Principles
[00150] Based on the examined household load profiles, it can be shown
that there are very little common behavioral features among different
electricity
users. However, a single user does demonstrate behavioral patterns over the
course of time. The objective of the forecast algorithm, as applied within the

system and method of the invention, is to utilize a pattern recognition system
to
exploit this fact. Hence, an unsupervised learning approach is suggested based

on statistical analysis.
[00151] Patterns can be found in different frequencies and time-
resolutions.
For instance, a pattern can be found in hours of every day¨time-resolution of
an
hour, period length of a day (Figure 2); another example is detecting a
pattern in
days of a week¨time-resolution of a day, period length of a week (Figure 3).
[00152] While multiple patterns can exist simultaneously, the combination
of
patterns varies for different households. For instance, while one user may
demonstrate a very strong hourly behavior every day, another user may not
demonstrate a clear hourly pattern at all. Nevertheless, the same two users
might
have strong weekly-day patterns. Consequently, the present invention provides
a
method and system which can analyze all possible patterns and extract and only

the appropriate ones for each user.
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[00153] To make the
matter more complicated, on a single frequency, a
user might demonstrate a behavioral pattern in parts of the period length, and
no
behavior at all in the remaining. For instance, most users have a very strong
behavior over sleeping hours (highly repeating, low deviation), but no clear
behavior during the daytime (non-repeating, high deviation). Figure 2
demonstrates this fact as the deviation in early hours of a day is rather
minimal,
while the deviation of the later hours of the day varies significantly.
Therefore, the
forecast algorithm should be able to integrate the detected patterns in the
highest
time-resolution (smallest values for fl defined below), and for each time unit
in the
future use their strongest patterns to make a forecast. To demonstrate this in

Figure 2 and Figure 3, the forecast value for 5AM Tuesday should be entirely
based on the pattern in Figure 2; the forecast value for 3PM Thursday should
be
mostly based on the pattern in Figure 3; and the forecast value for 1A1V1
Wednesday should take advantages of the both patterns.
Pattern Analysis
[00154] As noted above,
patterns exist in different frequency and time-
resolutions. The consumption data, provided in a resolution, is presented by
Ca:
cc'._ (clx, Ct, Cfc,11
The first step, then, is to take this data to the correct time-resolution for
the pattern of
interest, [3:
new size 14 = ¨ Cl3 = ...,
P)
r
21
, Cia k [1,4 cia
..1
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[00155] Note that 13 ?._ a, since the desired pattern
resolution is never
smaller than the original data's resolution. Next, the mean ( ) is calculated
and
the deviation (s) of each (3-sized time interval (t), within the period length
P.
a
for t = [1. & --1 and d --I:F-1
ig -0-ff P = a
ld-i ci-1.
r13
gt = ziX-(i=d+t), st
1=0 i=0
[00156] A more algorithmic way of representing p, and s is:
\
Vi: i ,01 = t it, = ct, st= _c_t_ii I(c-¨,,,,)2,
1
I ,
[00157] A forecast of the future consumption can be made based
on the
mean and standard deviation. While a low standard deviation (St) indicates a
highly repetitive behavior in the given time resolution and offset, a high
deviation
indicates no significance pattern.
[00158] Once the standard deviation is acceptable at the given
time-interval
1,
t of ¨ frequency, the mean value (At) can be used as the prediction of the
users
P
future behavior at the same time-interval of future periods.
Pattern Analysis Example
[00159] The following section demonstrates an example of the
above steps.
Using an hourly data provided for a period of a month (N --= 720 hours), the
behavioral pattern over days of a week are investigated (resolution: 1 day or
24
hours, period length P= 1 week or 168 hours).
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Table 1 shows a portion of the raw data [ref: LM SFD E (ID 2002282), 30-6-2006
to 29-
7-20061 Ca, where a = 1 hour.
Table 1 - Raw Consumption Data, Resolution: Hours
A5.5 Cf..g =b.5 05 0.5' 0.5 1.7 ' 71+:4'
3 . 7 9 4 4 5 7 0 6 3 1 2 4 7
õ
4.8 3.4 4.4 2.2 4.6 7.0 5.3 3.7 1.5 1.3 1.0 0.6 0.7 0.5 0.5
2 1 6 3 7 7 8 5 6 9 5 5 7 5 8
0.5 2.3 3.9 1.1 2.7 1.5 1.6 1.3 4.3 3.4 1.2 1.3 1.5 3.7 7.0
7 0 5 5 8 4 0 9 5 3 9 7
Wu,
3.3 3.9 1.2 1.5 1.7 0.9 0.9 0.9 0.6 2.7 4.5 4.8 6.1 6.1 4.7
5 1 4 0 4 1 2 0 3 5 2 7 3 0 5
.===
6.4 7.2 5.7 5.9 0.8 1.3 0.8 0.5 0.5 0.7 0.8 3.5 3.6
4 5 1 7 3 6 8 2 8 7 6 0 4 8 2
48 14 12 10 12 2I 30 11 36 ZI 40 24 18 1.8 13
8 60. 4 75 2 2 3-
Table 2 shows co for resolution 13= 24 hours:
= 30
Table 2- Consumption Data at 1-Day Resolution
111 Fri [2] Sat [3] : [4] [5] [6] [7] Thr [8] Fri [9] Sat [10]
Sun j Mon Tue Wed Mon
56.18 '1 51.502 :72.86 81.64 I 56.90 59.756 57.13 62.92 - 55.822 42.-429
6 4 9
I [11] [12] ; [13] !, [14] [15] [16] [17] [18] (19)
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Tue Wed ! Thr Fri Sat ' Sun ! Mon Tue Wed Thr
48.70 52.075 57.18 ! 69.254 53.89 41.883 60.563 39.81 55.973 61.194
1 i 1 i 7 i 6
[21] [22] [23] ; [24] [25] [26] [27] [28] [29]
[30]
Fri Sat Sun Mon Tue Wed Thr i Fri , Sat
Sun
44.29 54.078 . 44.72 51.656 37.44 53.175 60.031 i= 42.49 72.379 47.006
2i 5 4 4
Finally, Table 3 presents the values for At and s t :
t = [1, = [1, 7], d = 4
Table 3 - Calculated Mean and Standard Deviation
T /1 S
1 (Fri) ; 53.29 12.5
2 (Sat) ; 56.77 4.23
3 (Sun) 48.48 6.34
4 (Mon) 56.88 13.0
(Tue) 51.90 20.4
6 (Wed) 54.53 2.28
7 (Thr) 59.54 1.69
[00160] As shown in previously in Figure 3, the above load profile
demonstrates a strong repeating behavior on Wednesday and Thursdays
(swed = 2.28, sthr = 1.69), while the behavior on Tuesdays is the least
predictive
(Stu, = 20.4). Therefore, if a prediction is to be made for a coming
Wednesday,
f1 wed = 54.53 can be used as a reliable estimate.
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Trend Analysis
[00161] Many behavioral changes occur continuously over the course of
time. An example of this is shown in Figure 4. A likely explanation for such
smooth transitions is the correlation between consumption behavior and
seasonal
factors such as weather.
[00162] When a user's consumption changes, the average-based 'Pattern
Analysis' method would require some time to adjusts its forecasts. This is
because the new behavior should represent a significant part of the history-
data,
before it shows itself in the mean-values. Therefore, the forecast would lag
behind such changes.
[00163] In order to decrease the response time, consumption trends can
be
taken into account within the present method and system. While Pattern
Analysis
examines change in consumption over time, Trend Analysis focuses on the rate
of change. As in the above example, the user's consumption increase in
December is easily predictable in the previous month. Hence, detecting trends
helps the forecast respond to changes quickly, thus increasing the performance

index by minimizing error.
[00164] Trends can be examined at different time-resolutions and
polynomial orders. Lower time-resolution (large le values) make the trend
analysis less sensitive to noise ¨ highly deviated data with insignificant
forecasting value. Moreover, higher polynomial orders are more responsive to
change, but also more sensitive to noise.
After adjusting the consumption data's resolution (same as the initial step in
Pattern
Analysis), linear regression is used to detect the trend:
n: polynomial order, c = ao = xn + a, = xn-1+-- = -Fan-1 x + an
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where x is the time and c is the consumption. The least-squared solution to
the above
polynomial is:
t cli i 1 x, xi ... xli 1 ri
. = 1 Xy Xy ... Xy ai
nr data points, -4 Y = XA
...
Cm 1 Xnt 2g.õ
XTC = XTXA A = VT XriXT C
[00165] For instance, the solution to a first order polynomial would
be:
I Cc211 111 XX 1 liact0-
Cmj 11 xmfian_
/ 1 x11)-1
[al = [ 1 1 ... 11 1 x2
solving for a's: [ 1 1 ... 1 1 [col
.4 \, .7C1 Xy === Xinf : i 1X1 Xy ... Xrni LC3.1
1 Xrn
a:1 =.... (rit:=1,i) (riZ., 4) ¨ (E1:-.i xi)(E7.1, xivi)
{ n rn x ? ¨ (FT x.)2
¨t=1 t µ¨t=1 Li
nri x-y--TizixocrilixLyo 1 l
[00166] Having solved for a's, the polynomial equation can now be used
to
determine the consumption at a given time (x) in future.
first order: tr(x) = cto = x -I- al
n-th order: tr(x) = an = xn + al = xn-1 + === + an_i = x +a7,
[00167] The accuracy of the estimated trend line can be measured by:
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ESS =Z(tr(xi) ci)2 = CTC ¨ (xTX)-1XTC XTC
Data Expiry
[00168] User consumption behavior changes over the course of time and
factors such as season can play a significant role in the consumption. As
collected load data age, they can potentially become less accurate due to
changes in user's life style, season or weather. Therefore, a time will reach
when
the data 'expire' ¨ e.g. the aged data will not be considered in analysis any
more
within the method and system of the invention.
[00169] There are various advantages and disadvantages to eliminating
old
data:
a. Advantage: the forecast algorithm responds quickly to changes in behavior.
b. Advantage: less memory is required for storing the aged data.
c. Disadvantage: a temporary change in behavior ¨ a big enough change that is
not sustainable enough for forecasting consideration ¨ can significantly
affect
the forecast (i.e. noise sensitivity).
[00170] Each type of pattern or trend calculation can have its own data

expiry policy, since different analysis might require various sizes of
historic data in
order to work well.
Forecast Responsiveness
[00171] An important and rather controversial question about PowerTab's

forecast algorithm is how responsiveness the forecast shall be to changes in
consumption behavior. The method and system of the present invention provide
a balance between a non-responsive system and a highly responsive one. For
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instance, it is not desirable that the forecasted bill increases vividly when
a user's
consumption doubles for an entire week, nor is it desired that the forecast
change
notably when a user has done two hours of laundry.
[00172] In long term, non-responsive systems produce more accurate
forecasts, while the highly responsive ones occasionally react to noisy data
and
produce inaccurate predictions that lower their overall performance. This is
similar to any Control System in which fast response time causes overshoot.
[00173] A primary objective of the Smart Budgeting method and system is
forecasting the electricity bill based on users current behavior to inform
them of
how much they will be charged if they continue to consume this way.
Therefore, rather than trying to forecast with least possible error, the
forecast
value/end product of one aspect of the present invention, ties itself greatly
with
user's current consumption behavior. For example, if a user over-consumes for
a
few days in a row, our system should increases its bill estimation to warn the
user
about their behavior. If the users over-consumption days are temporary, the
increased estimation introduces error and a non-sensitive forecast system can
eliminate that error; however, based on the intended application of the
forecast
system, a consistent over-consumption behavior is worthy of warning and hence
the error factor is not as important as this objective.
[00174] Two factors play a role in the forecast responsiveness: trends,
and
data expiry periods. Trends play the most significant role in responsiveness
since
they attempt to forecast based on the recent user behaviors (e.g. the last
data
points in the trend calculation considerably affect the trend forecast). This
makes
the trend analysis very sensitive to noise ¨ highly deviated data with
insignificant
forecasting value. One way to lessen this over-responsiveness is to use trends

on low-resolution data (large p value such as days or weeks) and hence
reducing
the noise sensitivity.
[00175] Additionally, the length of the data history used for pattern and

trend calculations is another important factor in sensitivity of the forecast
system.
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This concept was introduced in the Trend Analysis discussion above. While the
'noise sensitivity' created by Data Expiry can be disadvantageous to a generic

forecast algorithm, it will in fact be advantageous to the intent of the Smart

Budgeting method and system. The present invention does indeed require
responsiveness to user's behavior in order to inform them of the consequences
of
their current consumption habit. And therefore, the only forecasting drawback
of
the Data Expiry is in fact useful for PowerTab. The expiry period has to be
chosen delicately in order to maintain reasonable responsiveness.
Integration
[00176] Various patterns and trends can exist for any given user at any
given time. An important step toward a reliable forecast is integrating all
patterns
and trends to obtain a concise outcome. The integration needs to be
proportional
¨ a more accurate pattern/trend should affect the outcome more significantly
than
a less accurate one, The accuracy of a pattern is inversely proportional to st

(standard deviation) at given time, and the accuracy of a trend is inversely
proportional to ESS.
[00177] The Smart Budgeting method and system starts with integrating
all
patterns first, before applying the trends:
fork patterns and trends,
[uuõ , us, : mean and standard deviation at time x for pattern u
vtr(x), 'ESS: trend estimate and error at time x for trend v
Li:pattern, up, =
u:trend, up, = utr(x)
k upx = --I¨

I. u sx
W (X) = , P (X) = __
S W (X)
x u=
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where w(x) represents the total weight of all pattern forecasts at time X, and
f(x)
represents the final forecast value. The above method applies to
patterns/trends of the
same time-resolution. Those of varying resolution can be combined when they
are
converted to the lowest time-resolution:
p" = a: resolution, fl: new resolution, /3 > a
(t ¨ 1) = I-3- < x < t = f-3- g uPd
a a
to,a lopff
_________________________ a.

D( = PX = ___ , = l sf vfi
Pt:(xGt) c). t:(xct)
[00178] Preferably, the following steps are taken to integrate all
patterns:
= Integrate all patterns of the highest resolution. Since trends are only
used at
lower resolution, no trend would be integrated at this step.
= Integrate all patterns/trends of the next highest resolution.
= Use the technique for varying resolutions to integrate the last two
outcomes.
= Repeat steps b and c until no lower resolution pattern exists.
[00179] Since user behaviors vary diversely, not every pattern or trend

analysis may highlight a useful repeating behavior. However, using the above
integration approach, many patterns and trends proportional to their
forecasting
strength can be integrated, and in a dynamic, time-efficient manner.
[00180] If after further examination of user behaviors it is discovered
that a
substantially small group of users has a very distinct yet strong behavioral
pattern, an appropriate pattern analysis component can be added to the method
and system for those users. This addition would strongly improve forecasting
performance for those niche users, while not at all degrading the performance
for
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all other users who do not behave that way. This feature of the present
"integration approach" makes the method and system very sustainable for future

research and customization to new markets.
Examination
[00181] Using the load profile data of 17 households over a course of a
year, the above principles were adopted to PowerTab's specifications. Two
patterns (daily-hours and weekly-days) and one trend (first-order weekly-
based)
were found sufficient for an accurate forecasting capability.
Pattern: Daily-Hours
[00182] The highest possible time-resolution of a forecast is equal to
the
highest time-resolution of the analyzed patterns. Therefore, to be able to
make
hourly forecasts, patterns of hourly behavior were preferably analyzed.
Clearly,
the most useful hourly-based pattern can be calculated for a period-length of
one
day ¨ hence the name Daily-Hours. Daily-Hours analysis has proved itself very
helpful for forecasting, because time of day is one of the most significant
parameters for user's behavior.
[00183] A considerable majority of users have a very low-deviating
behavior
over sleeping hours. Yet, the behavior during the daytime varies. Figure 2
demonstrates this fact as the deviation in early. hours of a day is rather
minimal,
while the deviation of the later hours of the day varies significantly.
[00184] The data-expiry limit for daily-hour analysis is set to 30 days
(i.e.
data older than 30 days are not used for this analysis). The 30 days limit is
set in
order to keep the algorithm responsive to changes in daily behavior, while
making
sure it is not too sensitive to noise and outliers.
[00185] The data-expiry limit for weekly-day analysis is set to 60 days
(i.e.
data older than 60 days are not used for this analysis). This limit provides
an
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average of 8 samples for each day of the week, which is rather minimal for an
accurate averaging. Meanwhile, extending the data-expiry beyond 60 days is
dangerous because after two month, those data can be obsolete for forecasting
purposes (i.e. high possibility that user's consumption behavior has changed
significantly).
Trend: First-Order Weekly-Based
[00186] Based on data acquired, a trend line on a weekly time-
resolution
and using a first-order polynomial fitting has proved itself very useful for
accurate
forecasting. Any time-resolution higher than one week is prone to frequent
error
due to noise and outliers. Moreover, 1st order, 2nd order and 3rd order
polynomials
were experimented with. While 2nd and 3rd order perform better estimates at
various occasions, the overall performance of the 1st order regression was
better.
[00187] The data-expiry limit for the weekly trend analysis is set to
60 days
(i.e. data older than 60 days are not used for this analysis). This limit
provides 8
data points (weeks) for trend-line calculation. This is rather minimal for
accurate
trend estimation. Meanwhile, extending the data-expiry beyond 60 days is
dangerous because after two month, those data can be obsolete for forecasting
purposes (i.e. high possibility that user's consumption behavior has changed
significantly).
[00188] Importantly, it was discovered that the weekly-based trend
analysis
creates a suitable responsiveness for the forecast algorithm. As soon as a
user
spends a good portion of a week (3 days or more) over-consuming, the weekly
consumption for the most recent week increases, causing the trend-line to
shift
upward. This effect increases the forecast estimate of the upcoming days. The
increase helps warn users about the value of their next bill, if they continue
their
recent consistent over-consumption behavior.
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Absence Detection
[00189] Two
types of patterns exist: repeating, and non-repeating.
Repeating patterns are useful to forecasting (e.g. sleeping hours), while non-
repeating patterns - statistical outliers - are misleading. Outliers are
infrequent in
nature, and since our approach is based on averaging, they are insignificant
to
the outcome.
[00190]
Behavioral outliers exist as well ¨ non-repeating behavioral patterns
that deviate from standard.
However, as opposed to statistical outliers,
behavioral outliers are not always infrequent. Vacation periods are a perfect
example of non-infrequent behavioral outliers. Due to their length in time,
these
behavioral outliers can affect the forecast outcome significantly. For
instance, a
three-week vacation period can completely mislead the forecast algorithm's
expectation of the user's behavior.
[00191] In
this case, an absence detection mechanism is implemented with
the Smart Budgeting method and system of the present invention which excludes
from the forecast algorithm, the periods in which no user is at home. One can
easily spot absence periods when looking at the consumption graphs. That is
due to human brain's highly capable pattern recognition skills. Absence
periods
share two characteristics: first, the usage is observably lower than typical
consumption periods; this property, however, does not help detecting absence
times since both the magnitude of consumption, and the ratio of absence
consumption to typical consumption, are rather hard to define as they vary
from
one user to another.
[00192] The
second characteristic of an absence period is its low deviation
in the consumption record; since no person is present at home, the changes
occurred in the consumption are significantly smaller than that of a typical
period.
Some time-varying electric appliances such as thermostat heater or air
conditioner, however, can introduce deviations to the power consumption during
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absence periods. Two solutions may be introduced to the Smart Budgeting
method and system, and when combined, they can solve this problem:
= Defining consumption deviation tolerance ranges based on a percentage of
the typical consumption deviation. If deviation is higher, someone is present;

if it is lower, no one is.
= Use of the previous day's absence status: If a user was absent yesterday,

s/he might be on vacation and hence s/he is more likely to be absent today.
[00193] Utilizing fuzzy logic, this two decision methods can be combined

based on the following table:
Table 4 - Fuzzy Logic Table for Absence Detection
YESTERDAY
T Yes Likely Unlikely No
O Yes True True It True 1
False
= Likely -true True -alseZI1iiII
False
A Unlikely True 1 False 1 False False
No True False False I- False
The four fuzzy sets of "Yes," "Likely," "Unlikely" and "No" are defined as
provided in Figure 7.
[00194] Finally, to calculate the values for "today" and "tomorrow'
variables,
we use the following set of equations:
LEP iEP
Stoday = S(P = today's consumption data)
Syesterday = = yesterday's consumption data)
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salt = s(P = entire consumption data)
Syesterday
today = -stoday ,
yesterday =
Salt sau
[00195] To elaborate on the above equations, the ratio of the today and
yesterday's hourly standard deviation, over the overall hourly standard
deviation.
If the ratio value is reasonably small in both days, that shows a lower than
usual
daily consumption deviation, which helps detect absence of users.
Light Indicator
[00196] Among the most important features of the PowerTab is its light
indicator that provides instantaneous feedback to user's consumption behavior
to
help them lower their consumption within their targeted budget. The light
indicator has two states: Red representing overconsumption, and Green
representing proper consumption. When the light is red, the user is expected
to
take measures to lower their consumption; and when the light is green, the
user is
notified that their current behavior would achieve the target.
Budgeting
[00197] The light indicator uses the user-inputted target bill value,
the dollar
consumption so far, and the user's forecasted behavior in order to determine
an
hourly budget for the remaining part of the billing cycle. The operation is
performed as follows:
given: t E Q, Q: remaining period in the current billing cycle
T: target budget ($), S: spent so-far ($), gt:cost of 1 kwh at time t
ft: forecasted consumption (kwh) at time t, st: forecast's deviation at
time t
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[00198] First, within one embodiment of the Smart Budgeting method and
system, the remaining dollars to be spend during the remaining days of the
current billing cycle is calculated:
R: remaining budget ($), R = T ¨ S
[00199] Next, a budget for every remaining hour of the billing cycle,
based
on both the forecasted spending and its possible deviation, is specified:
[00200] Case 1) If the unconsumed budget is more than forecasted
spending: the extra money will be divided between all remaining hours,
proportional to the forecast deviation. For instance, since the deviation is
small
during sleeping hours, not much of the extra money will be devoted to those
hours since the user clearly does not need much room there. However, during
hours where the user does not spend consistently, he/she will be given
additional
budget.
[00201] Case 2) If there is some money left in the budget (unconsumed
budget > 0), yet the left-over is less than the forecasted consumption: this
means
that the user is over-consuming, so his/her hourly forecasted consumption
should
be reduced. When giving extra money to each hour, this was allocated
proportionally to each hours consumption deviation. However, when shrinking
the consumption, the method and system of the present invention does it
proportional to the forecasted consumption itself. That is because one end
goal
of Smart Budgeting is to encourage the user to adopt a more conservative
behavior by saving at all times. Even during sleeping hours when the deviation
is
low, turning off an extra appliance might be the key in achieving the target
bill,
and therefore he/she is asked to lower every hour of consumption by a certain
percentage rather than considering the deviation patterns.
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[00202] Case 3) Finally, if the amount of money spent so far is more
than
the total budget (remaining budget < 0), then the user cannot achieve his/her
goal
and a $0 budget for every remaining hour is specified.
[00203] The above policies are implemented within the Smart Budgeting
method and system and represented in the following equations:
F: forecasted spending ($), F = ft gt,
tEQ
R > 0 = R F: Bt = ft = gt + p Sc
v, , where p = R ¨ F
LuEQ 5u
R > 0 = R < F: Bt = ft = gt x ¨F
R < 0: Bt = 0
State Determination
[00204] Once the consumption budget of the remaining billing period is
determined, the light indicator should decide whether the user is over-
consuming
(red or green light). The most important criterion for state determination is
whether the consumption of this hour is less than or equal to this hour's
budget:
under-consumption: C = gnow
rule #1:
over-consumption: Cõw = gnaw > BTLOW
[00205] Considering the following scenario: a user's budget is $70. It
is the
6th week of the 8-week long billing period, and she has spent $30 so far.
Therefore, the user has 2 weeks left and $40 to spare, which means her
consumption can triple and still the target budget will be met. If the user
decides
to do 3 hours of doing laundry, cooking dinner, ironing and watching TV all at
the
same time, she will surpass her hourly budget. Should she be warned about
this?
[00206] A reasonable answer to this question is 'no', because the user
has
a considerable amount of budget left and it is very clear that a mere 3 hour
of
overconsumption would not challenge the achievability of her target bill, due
to
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her fine record of under-consumption. However, if the above rule were to be
considered independently, the light indicator would go red which would come as
a
surprise to the user. Such unreasonable judgment by PowerTab can seriously
challenge its trustworthiness for helping users conserve, which is its
primarily
objective.
[00207] To rectify the above problem, a new criterion is introduced
within
the method and system of the invention which adds a `consistency' factor to
the
. decision of whether a user is over-consuming. In other words, not only the
user
should be consuming more than the current hour's budget, she should be
consistence in it for a short period to receive a warning. To do so the light
indicator looks into user's last 24 hours of consumption, and if there is a
left-over
budget within this time, it uses that to tolerate the current over-
consumption:
under-consumption: E ct gt Bt
tElast 24 hours tELast 24 hours
rule #2:
over-consumption: Ct = gt > Bt
tElast 24 hours tE/ast 24 hours
[00208] The light indicator state will be determined based on the rule #1

and rule #2, as follows:
red: (role#1= OC) = (role#2 = OC)
state:
green: otherwise
[00209] Based on the above system, few hours of over-consumption can be
tolerated if the user's overall behavior is conservative enough. The following

scenarios elaborate the capabilities of the Smart Budgeting method and system:
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[00210] Case A: A user's budget for the last 24-hour period was $2.4 and
his current hour's budget is $0.12. He has consumed $2.1 during this day. If
he
consumes more than $0.12 this hour, rule #1 would indicate overconsumption;
however, rule #2 would not, and therefore the light will be green. However,
this
tolerance will be exhausted as soon as the user consumes anything more than
$0.3 within this hour (excessive overconsumption).
[00211] Case B: If the above user has spent $3 within the last day, rule
#2
would indicate overconsumption, even if the user is spending less than his
$0.12
budget for current hour. However, the light would still show green, because
the
extra consumption has already been deducted from user's future budgets (hence
shrinking the 0.12$ slightly). Thus, if the user is currently spending 0.06$,
he
would correctly see a green light indicating that if he continues to do what
he is
doing 'right now', he would be achieving his consumption goal. But as soon as
he
passes the hourly budget, he would see a red-light since there is no tolerance
to
further overconsumption.
[00212] Case C: If a user has a $100 budget, and he has only spent $30
seven weeks into the period, he would have a $10 per day budget for the
remaining days. If he spends $3 in 23 hours, he would still have $7 to spare
in
one hour which means he would not get an overconsumption alarm that easily
(i.e. very high tolerance).
Dependency on Forecast Algorithm
[00213] As demonstrated herein, the forecast value for each hour is the
base for its budget determination. An alternative is to use no forecasting and

evenly divide the remaining dollars of the budget over every hour. A user's
hourly
behavior is not even; in some hours the consumption is low (e.g. when
sleeping)
and in some hours the consumption is high (e.g. evenings). However, if every
hour is budgeted evenly, the system would always indicate a green light during

sleeping hours, even if the user has forgotten to turn off the TV; and it
would
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always show red light during evenings, even if the user has consumed less than

usual.
[00214] The other side of the extreme is if it is known 'exactly' how
the user
is going to behave. In that case, the user would never see a red light if his
future
behavior would be meeting the budget requirement, even when he is consuming
excessively for a few hours ¨ since his behavior and excessive consumption is
known and expected, it is known that it would not cause him to go over the
budget. However, if the user's behavior leads to surpassing the budget, the
system would shrink every future hour's consumption by a needed percentage to
meet the budget. It would then use the light indicator to encourage the user
to
follow within those defined limits. If it is the beginning of the billing
cycle, the user
would see green light every now and then. However, if the end of the cycle is
approaching and the user is still over-consuming, the saving percentage grows
higher and higher and it would become almost impossible for the user to lower
his
consumption to that extend. Therefore, no green lights will be shown anymore
indicating that it is not very likely for the user to meet his intended
budget.
[00215] This is the ideal case, because the light indicator's purpose to

answer the following question is met perfectly: is the user going to go over
his
budget? The more accurately the user's behavior can be forecasted using the
Smart Budgeting method and system, the less 'false positives' and 'false
negatives' would show.
Potential Features
[00216] Using the same principles used in forecasting, absence detection

and light indication, the following features can be implemented as additional
embodiments of the Smart Budgeting method and system:
[00217] Sleep-Prep: The system can determine the usual sleeping hours
and patterns of a user. Hence, during the hours in which the user usually goes
to
bed, an icon can appear on the screen showing whether the household is 'sleep
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ready'. The system determines the sleep-readiness by analyzing a user's usual
sleeping pattern and his budget for those hours. If the current consumption is

similar to that of the user's usual sleeping hours and he is within the
budget, the
system provides a positive feedback. However, if an extra light is left on, a
warning can be displayed to notify the user
[00218] Leave-PreP: The same idea as the 'sleep-prep' can be applied for
when the user is leaving home especially for longer periods (e.g. vacations).
In
this case, the user might need to press a button on their In-Home Display,
smartphone or tablet app, etc. to ask for verification that the house is 'at
rest'
(minimum power consumption). The system then analyzes previous absence
patterns to determine whether an unnecessary appliance is left on or if house
is
"leave ready". The system could also analyze user's behavior and detect when
they may leave the premises (based on previous behavior, or other available
signals) and proactively inform users if their home is "leave ready.
[00219] Today's Performance: A simple addition to PowerTab can provide
a feedback about user's every day performance rather than that of the entire
billing cycle. The system can interpolate today's consumption over the
remaining
days of the billing cycle and generate a forecast based on today's
performance.
This forecast would vary greatly from one day to another and is not to be
trusted
as the final bill's value. However, it can help user understand how he has
performed today. Also, this would make the device interactive as the users
will
have a daily-based challenge; they can set new 'records' by trying to lower
than
number, and they would not need to wait longer before seeing the effect of
their
effort. Users may also use social media to share such data and "compete" with
neighbours based on performance indicators and other power usage metrics.
[00220] Absence Battery Saving: When the forecast algorithm detects
that the user is absent, it can turn PowerTab off in order to save battery. As
soon
as someone comes back, the system can detect that by observing the sudden
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jump in the consumption (turning on lights, etc.) and the device can turn
itself on
again.
[00221] Sleeping-Hours Battery Saving: The same absence battery
saving idea applies to the sleeping hours. Again the device can forecast
sleeping
hours, detect it when a user goes to bed, and turn itself off until changes in
the
consumption indicate the user's awakeness. The PowerTab can automatically
turn its display on during morning hours when the user wakes up because it is
likely for the user to pass by.
[00222] Interfacing With Appliances: Provision and conveyance of
information about user's daily behavior to other appliances. Using the
PowerTab,
all appliances can be triggered when user sleeps, wakes up, leaves home, goes
on vacation, etc.,. .and all this information is detected by the PowerTab
without
any user interaction.
Preferred Aspects of FHMM
For a FHMM with N HMMs, there is the following statistical model:
nr) Pcsr'. Multi(cP)),
4.1 : F (4 = j1.4a2 = i) = 11ft4P),
P At) = "a. E.
where 41) is the status variable for the ith HMM at time t, yris the latent
output for this HMM at time t, and sit is the true observation at time t of
this
FHMM, which is the summation of all yt(i)'s. N stands for normal distribution
and Multi stands for multinomial distribution.
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All the parameters in the statistical model described above are estimated
from the real power data. The EDHMM-dif model is designated to perform
estimation from aggregated power data.
EDHMM-diff
For the HMM within the FHMM, there is devised an EDHMM-diff model (i is
omitted here)
=0.
= =
zNYt = Yt Ye-it
3.9 rt-i) = Ar(11 rt )'
= A).
where most of the variables have the same meaning as the ones noted
above. It is worth noting that in EDHMM-diff the differential observation Alit

. Pk(d) is modelled. Pk(d) is the probability for this HMM stays at status k
for
a duration d, where xt-t+d., means all the points x from time t to time t+d-1,

which are d points in total. All the parameters of EDHMM-diff are estimated
from the real power data, including the probability distribution Pk(d). To
estimate a single HMM from the aggregated power data is not possible
using all the previous methods of the art, hence the importance of the
EDHMM-diff to this preferred method of LD.
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In operation, the use of EDHMM-diff to "detect-and-tune" is as follows: for
an EDHMM-diff model, the set of parameters 19 can describe the whole
model. Within the scope of the invention, there is provided a library of O's
for a plurality of common appliances, which are estimated from power
signals collected from standard appliances. The true 0() for an appliance i
in some house may be different from the OICIL)b so prepared, due to the
difference between real appliances. It is required to estimate the true OW
from the real aggregated power data in testing. Taking a fridge as an
example First, a set of sliding windows on the power signal is defined, then
the signal within the window is denoted by Ymin for window s. Second, the
likelihood for this signal clip is computed given model Oifibridge y EDHMM-
diff,
the likelihood is
(1)
If this likelihood larger than certain threshold, it is kept as a valid set
Sv.
After detecting valid signal clips through the power signal, a set Sy is
acquired that contains signals generated by the fridge in this house. Then
S, is used to estimate the EDHMM-diff to get a true fridge for this house,
which is different from the e[ibr119e At last, this Ofrlage is used as the
estimated parameters for FHMM to perform disaggregation.
Estimation and inference of EDHMM-diff
A forward-backward algorithm for difference HMM with explicit duration.
1 Forward
Definition:
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(.1) = P(,5t
14(0) Ptst st+t
P(}7t-ii+1:02-1-d)
tiz,j
d) ¨ ____________________________ -
-zit(d)
PITt ¨d+1:t1 ¨d = =j)
P(t¨d+l:t IY2q¨d)
Induction:
6t(i) E E
iÃS/{J}
(i (Oftej
.it(d) = E E :OP) (014(i ,itd)
iEsh
Notes on derivation:
DP.1(d)ut(i,d)
= P(A1/4-4 = i; 8t¨a+1 .111-2,t-OrTr = = i= 8t--.e1+1 PP(11¨d+11tI6t¨d =
j)
88-d+13) jiY)-t-d)PrXt-d+.1:tki-d = .9t¨d+13 = j)
= P(.3t¨d = i,st-d4-11 = 1-t¨d+1,t1Y2:t ¨a)
Initialization:
P(2 = JIY2)
=Ep(y:21,, =i,82=AP(82=As1
agU) ¨ Ei ct2(i
Likelihood:
510) r(YdY2,t--1)
P(Y2:7') H (1)
t =3
2 Backward
Definition:
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P(Yt4-i-Tist = i)
Rt(i)
1 ..6,-1-1:7112:11
_ P(rt+-ifrist i,st+1 =1)
P(Yt+4,TIY2,tt)
Induction:
Fit (i, E Fit +d(i, j,d)Pi(d),at+d(i)
d-GD
ilt(j) = E
kGs
Notes on derivation:
¨ P(Yt4,1=4_dl,9t =
,
PO' t+i:t4d1Y2,t)
pi(d)¨ Po-t+i =4st = R+t =
- P(Yt+d-1-1.T ISt+d = j)
A+43) ¨ = ,
t+d+i,ri 1, 1214-d)
rit+40,i,d)Pi(d) P(sE4-2:t+d = fist = 8t+1 = jAt+d(id,t1)
PM.4_1:td. 7- = (1St = = '1)
14440, j, COPMA+,10) NY-t+1:f.4-d= St+d = =(18t it = -
0P(Y14d+.1:T18t+d= j)
P(rt-f-Lt-i-dlY2AP(Yt+d-1-1:7`1121-1-d)
P(Ii+LT.T = tilSt =- St+1
figij)
Initialization:
= PCYrIsr-1 = sr = i)
P(YilYi :T-1)
= (" = j,
= P(rrisT-1 = j)
3. Estimation
Definition:
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P(84 = AY2:7') =
nt = P(st = _1! (11Y2-r) = (11(j: d).-etCi
= P(At=
& (is = P(6t = jt 41 jI12:79 07 (0)
Parameter Updata:
Ewi,J)/
Eqt(i,d)
d
P = El,t_itiNun-t/E-tt-lobt(i)
t =3
2 2 /
(Ti) u)(Y t - ( U)
= 12(1) / E11(j)
=
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[00223] While the forms of method and system described
herein constitute
preferred embodiments of this invention, it is to be understood that the
invention is
not limited to these precise forms. As will be apparent to those skilled in
the art, the
various embodiments described above can be combined to provide further
embodiments. Aspects of the present systems, methods and data collection
means (including specific components thereof) can be modified, if necessary,
to
best employ the systems, methods, nodes and components and concepts of the
invention. These aspects are considered fully within the scope of the
invention as
claimed. .For example, the various methods described above may omit some
acts, include other acts, and/or execute acts in a different order than set
out in the
illustrated embodiments.
[00224] Further, in the methods taught herein, the various
acts may be
performed in a different order than that illustrated and described.
Additionally, the
methods can omit some acts, and/or employ additional acts.
[00225] These and other changes can be made to the present
systems,
methods and articles in light of the above description. In general, in the
following
claims, the terms used should not be construed to limit the invention to the
specific embodiments disclosed in the specification and the claims, but should
be
construed to include all possible embodiments along with the full scope of
equivalents to which such claims are entitled. Accordingly, the invention is
not
limited by the disclosure, but instead its scope is to be determined entirely
by the
following claims.
=
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Representative Drawing